Nitrate is one of the most frequent pollutants of groundwater, and in some areas, nitrate pollution is becoming a serious problem. Seeking new material and methods for improved efficiency of groundwater nitrate removal is a hot topic of environmental research. While graphene has been widely used in the processing of heavy metal ions in aqueous solution, its role in nitrate removal remains largely unexplored. In this study, we loaded micrometer-sized graphene with nanoscale iron particles (G-Fe) by liquid-phase reduction. The characteristics of nitrate reduction by the G-Fe composites were determined under different conditions using static experiments to reveal the reaction mechanism of G-Fe in removing nitrates. Results showed that the optimal load ratio of graphene with nanoscale iron was 5:1. Lower initial pH improved nitrate removal efficiency (NRE) to varying degrees and 100 % removal was obtained at pH 2.15. Dissolved oxygen (DO) had no effect on NRE. The effect of coexisting anions on NRE descended as follows: PO43−, SO42−, and Cl−. Kinetic studies showed that the reaction order between G-Fe and nitrate was about 0.45, indicating that the reaction involved complex redox reactions and adsorption/desorption processes, other than a simple first-order reaction. This study demonstrates the effectiveness of G-Fe composites in nitrate removal and establishes an advanced technology for groundwater remediation.
Abstract. In recent years, the frequency of landslide disasters has been increasing year by year due to the extension of human activities to the natural environment. Fast and detailed landslide surveys are important for landslide disaster prediction and management. There are many driving factors for landslide formation, and most of the current deep learning-based landslide identification methods use optical remote sensing images in a short period or a few types of fused data for prediction. Therefore the upper limit of accuracy they can achieve is low. This paper proposes a landslide identification network model based on the spatio-temporal fusion of heterogeneous data from multiple sources. The model takes observations such as time-series optical remote sensing images, DEM, geological formations, and meteorological data as inputs. To address the problems of non-uniform data forms and redundancy caused by time-series data, we design the temporal phase fusion module of coupled CNN-LSTM to fuse the temporal features of multi-source data based on the extraction of their spatial features. Subsequently, we design the spatial feature fusion module based on DCNN-DBN to realize the deep expression of temporal phase and spatial features of landslides and improve the recognition efficiency and accuracy of the network. Through experimental verification, the AUC value of our proposed model is 0.8976, the F1 score is 0.8352, and the MIoU is 0.8624. The evaluation results reflect that the model can provide support for large-scale landslide disaster investigation.
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