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[学术文献 ] Research on the competitive and synergistic evolution of the water-energy-food system in China 进入全文
ScienceDirect
Water, energy, and food are essential and strategic resources for human well-being and socio-economic development and form the water-energy-food (WEF) system with competition and synergy. The competitive and synergistic evolution model was developed to remedy the limitations in quantitatively analyzing the tradeoffs and synergies of the WEF system. Firstly, an assessment model was developed for measuring the synergy and competition of the WEF system based on the order degree of each subsystem (That is, the development degree of each subsystem) and synergy theory. Then the synergy evolution model (SEM), with the help of a logistic model and accelerated genetic algorithm (AGA) model, was developed to measure and identify the steady-state. Furthermore, an empirical study was conducted with 30 provinces in China as examples. The results indicated that the food subsystem had the highest average order degree (0.347), followed by the energy subsystem (0.305), and the water subsystem had the lowest (0.281). The degree of order of the three subsystems exhibited an upward trend in time and has differences in the spatial distribution. Also, the results showed that synergistic, restrictive, and competitive relationships exist within the WEF system. Areas with competitive and restrictive relationships are mainly located in South China and North China, respectively, within the relationship between the water and energy subsystems. The entire country showed a restrictive relationship between the water and food subsystems. The energy and food subsystems showed that the eastern regions with relationship, while the western regions with competitive and restrictive relationship. Finally, effective measures (e.g., optimize the industrial structure, continuing to implement the strategy of “storing grain in the land and technology”, and to hold the arable land minimum) are suggested to achieve the WEF system coordinated and sustainable development. We believe that the assessment model is also applicable to assess the other complex and dynamic system worldwide that involve multiple factors.
[学术文献 ] A dynamic bidirectional coupling model for watershed water environment simulation based on the multi-grid technique 进入全文
ScienceDirect
Water pollution is a critical issue of global importance. Water environment modeling is an important tool for studying water pollution in basins. Based on a review of existing water environment models, a dynamic bidirectional coupling model for the water environment (E-DBCM), consisting of an upland watershed module (UWSM) and a 2D downstream waterbody module (DWBM), was developed using the multi-grid technique. The computational domain was discretized with grids of different sizes, and different time steps were adopted in regions with different grid sizes. The UWSM was applied to describe the generation and transport of pollutants on coarse grids to improve the computational efficiency, while the DWBM was capable of simulating the hydrodynamic and pollutant transport processes on fine grids to obtain a high accuracy. These two modules were spatially connected by a moving coupling boundary. Two test cases were used to validate the performance of the proposed model, and the results indicated that the E-DBCM had satisfactory computing efficiency while maintaining acceptable numerical accuracy. The water environment of the Yanqi River Basin was evaluated using the proposed E-DBCM. The maximum percent bias was 10.31 %, which indicates that the E-DBCM is reliable and that the numerical accuracy satisfies the engineering demand. The computational efficiency dramatically increased by 90 % when the watershed was discretized using the multi-grid technique. It was found that water pollution problems in this basin were serious, especially during the flood season. Various measures should be taken to improve the water environment treatment and strengthen protection measures in the Yanqi River Basin.
[学术文献 ] Effect of barometric pumping on relative humidity in the loessal soil of the loess Plateau 进入全文
ScienceDirect
Monitoring experiments of soil air in column show that relative humidity (RH) of air in the soil of the Loess Plateau fluctuates with atmospheric pressure (AP), which is barometric pumping resulting in the vertical movement of soil air and the change of its RH. When AP increases, soil air is compressed and atmospheric air enters the soil, causing RH to decrease. When AP decreases, soil air expands and rises, causing the flow of moist air outwards from the soil and increasing RH. Therefore, RH fluctuates reversely with AP (the correlation coefficient between RH and AP can reach –0.74). This paper studies the effect of barometric pumping on RH in the loessal soil of the Loess Plateau, and reveals the mechanism responsible for the vertical movement of soil air. On a yearly timescale, the volume of soil air expands with a general decrease in AP from January to July, then compresses with an increase in AP from July to December. On a daily timescale, the air in soil shows bimodal fluctuations. Barometric pumping is the driving force underpinning the soil air movement, and therefore dominating its fundamental characteristics. From a preliminary establishment of model calculation, the volume of soil air movement occurring in the Loess Plateau is proportional to the amplitude of AP fluctuations, aerated porosity, and thickness of the local loess layer. The amplitude of air fluctuations in the soil is independent from the aerated porosity.
[学术文献 ] A novel model for water quality prediction caused by non-point sources pollution based on deep learning and feature extraction methods 进入全文
ScienceDirect
Non-point source (NPS) pollution is an important factor affecting the quality of water environment. In recent years, a large number of online water quality monitoring stations have been used to obtain continuous time series water quality monitoring data. These data provide the necessary basis for the application of deep learning methods in water quality prediction. However, the prediction accuracy of traditional deep learning methods is low, especially in predicting the water quality with NPS pollution. Aiming to address this limitation, a novel deep learning model named SOD-VGG-LSTM with the simulation-observation difference (SOD) modular based on physical process, the visual geometry (VGG) modular reflecting spatial characteristics, and the long short-term memory (LSTM) modular based on deep learning method was developed to improve the accuracy of the water quality prediction with NPS pollution. The established model can overcome the problem that mechanism models can not predict the changes of water quality on the hourly or minute time scale. The model was applied in Lijiang River watershed. Experimental results indicated that the proposed model had the highest accuracy in the extreme value prediction compared with the mechanism model and LSTM model. The maximum relative errors between the predicted and observed results for DO, CODMn, NH3-N, and TP were 8.47%, 19.76%, 24.1%, and 35.4%, respectively. The model evaluation demonstrated that the established SOD-VGG-LSTM model achieved superior computational performance compared to Auto Regression Integreate Moving Average model (ARIMA), Support Vector Regression model (SVR), and Recurrent Neural Network model (RNN). The evaluation results showed that SOD-VGG-LSTM achieved 3.2 - 39.3% higher R2 than ARIMA, SVR and RNN. The proposed model can provide a new method for water quality prediction with NPS pollution.
[学术文献 ] Valorization of fruit waste-based biochar for arsenic removal in soils 进入全文
ScienceDirect
Fruit waste disposal is a serious global problem with only 20% of such waste being routinely treated prior to discharge. Two of the most polluting fruit wastes are orange peel and walnut shell and new methods are urgently required to valorize such waste. In the present study, they where valorized via conversion into biochars at 500 °C (OPB500 for orange peel-based biochar produced at 500 °C and WaSB500 for walnut shell-based biochar produced at 500 °C), and evaluated for arsenic adsorption. A pore-rich surface morphology was observed with a low H/C ratio indicating high stability. Spectroscopic studies revealed the presence of minerals and surface functional groups (amide, carbonyl, carboxyl, and hydroxyl) suggesting high potential for arsenic immobilization. Adsorption studies revealed an arsenic removal efficiency of 88.8 ± 0.04% for WaSB500 exposed to initial arsenic concentration of 8 ppm for 5% biochar dose at 25 °C and 30 min contact time. In comparison, OPB500 showed slightly lower removal efficiency of 80.7 ± 0.1% (10 ppm initial concentration, 5% dose, 25 °C, 90 min contact time). Peak shifts in XRD and FTIR spectra together with isotherm, kinetic, and thermodynamic studies suggested arsenic sequestration was achieved via a combination of chemisorption, physisorption, ion exchange, and diffusion. The present investigation suggests valorization of fruit waste into thermo-stable biochars for sustainable arsenic remediation in dynamic soil/water systems and establishes biochar's importance for waste biomass minimization and metal (loid) removal from fertile soils.
[前沿资讯 ] 水利部：137亿元支持大中型灌区现代化改造 进入全文
农民日报
日前，记者从水利部召开的加快水利基础设施建设有关情况新闻发布会上获悉，今年水利部安排中央投资137亿元，支持493处大中型灌区现代化改造，已有349处完成招标，320处开工。此外，计划今年新开工的6处新建大型灌区已开工两处。党中央、国务院历来高度重视农田灌排设施建设，现已建成大中型灌区7330处。目前，全国耕地灌溉面积达到10.37亿亩，在占全国耕地面积54％的灌溉面积上生产了75％以上的粮食和90％以上的经济作物。“十四五”以来，国家持续推进大中型灌区现代化建设与改造，水利部会同国家发改委专门印发了《“十四五”重大农业节水供水工程实施方案》，明确提出“十四五”对124处大型灌区开展现代化改造。在水土资源匹配地区新建30处大型灌区，会同财政部印发了《全国中型灌区续建配套与节水改造实施方案（2021-2022年）》，支持461处中型灌区实施节水改造。水利部农村水利水电司司长陈明忠介绍，今年水利系统围绕粮食的播种生产，以大中型灌区为单元，建立了春灌台账，深挖水利工程供水潜力，优化灌区供水调度，合理配置水资源，做到了科学用水、计划用水、节约用水，有效保障今年春灌农业用水。截至今年5月底，全国大中型灌区春灌累积灌溉面积达到3亿亩，供水449亿立方米，全面完成今年春灌任务，为粮食生产尤其是夏粮的丰收提供了有效保障。
