Abstract: Population increase, climate change and soil impermeability are factors causing floods in large urban centres. Such places also always have water shortage problems. This research aims to evaluate the influence of rainwater harvesting in residential buildings on stormwater in a basin located in southern Brazil (Rio Cachoeira Basin). Urbanised and non-urbanised areas, soil types, curve numbers and time of concentration of each sub-basin were taken into account. Through the HEC-HMS programme, it was possible to calculate hydrographs for the base scenario (when there is no rainwater harvesting). Then, rainwater tanks for the residential buildings were sized using the computer programme Netuno. In the second scenario, there is rainwater harvesting in all residential buildings. Thus, the hydrographs for the second scenario were also calculated. The peak flow reduction potentials for the sub-basins ranged from 2.7% to 14.3%. The highest percentage (14.3%) did not occur in the sub-basin with the most extensive roof area; such highest peak flow reduction occurred in Bom Retiro sub-basin. In Bom Retiro sub-basin, there are more houses than multi-storey residential buildings. Even when considering the full potential of rainwater harvesting for roof areas of all existing buildings in the Rio Cachoeira Basin, the average potential reduction in peak flow was 7.2%. The conclusion is that rainwater tanks in residential buildings have little influence on stormwater runoff, and the stormwater runoff will be less affected when the area of the hydrographic basin is larger. Thus, the reduction in peak flows is insignificant when considering the flooding in the region.

Abstract: A large number of radionuclides remain in uranium tailings, and U, 226Ra and 210Pb leach out with water chemistry, causing potential radioactive contamination to the surrounding environment. In this paper, uranium tailings from a uranium tailings pond in southern China were collected at different depths by means of borehole sampling, mixed and homogenised, and analysed for mineral and chemical composition, microscopic morphology, U, 226Ra and 210Pb fugacity, static leaching and dynamic leaching of U, 226Ra and 210Pb in uranium tailings at different pH conditions. The variation of U, 226Ra and 210Pb concentrations in the leachate under different pH conditions with time was obtained, and the leaching mechanism was analysed. The results showed that the uranium tailings were dominated by quartz, plagioclase and other minerals, of which SiO2 and Al2O3 accounted for 65.45% and 13.32% respectively, and U, 226Ra and 210Pb were mainly present in the residue form. The results of the static leaching experiments show that pH mainly influences the leaching of U, 226Ra and 210Pb by changing their chemical forms and the particle properties of the tailings, and that the lower the pH the more favourable the leaching. The results of dynamic leaching experiments during the experimental cycle showed that the leaching concentration and cumulative release of U, 226Ra and 210Pb in the leach solution were greater at lower pH conditions than at higher pH conditions, and the leaching of U, 226Ra and 210Pb at different pH conditions was mainly from the water-soluble and exchangeable states. The present research results are of great significance for the environmental risk management and control of radioactive contamination in existing uranium tailings ponds, and are conducive to ensuring the long-term safety, stability and sustainability of uranium mining sites.

Abstract: The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights.