Multi-objective optimization-based reactive nitrogen transport modeling for the water-environment-agriculture nexus in a basin-scale coastal aquifer

Patterns of nitrate load variability under surface water-groundwater interactions in agriculturally intensive valley watersheds

Nitrate pollution is a significant environmental issue closely related to human activities, complicated hydrological interactions and nitrate fate in the valley watershed strongly affects nitrate load in hydrological systems. In this study, a nitrate reactive transport model by coupling SWAT-MODFLOW-RT3D between surface water and groundwater interactions at the watershed scale was developed, which was used to reproduce the interaction between surface water and groundwater in the basin from 2016 to 2019 and to reveal the nitrogen transformation process and the evolving trend of nitrate load within the hydrological system of the valley watershed. The results showed that the basin exhibited groundwater recharge to surface water in 2016-2019, particularly in the northwestern and northeastern mountainous regions of the valley watershed and the southern Beishan Reservoir vicinity. Groundwater recharge to surface water declined by 20.17 % from 2016 to 2019 due to precipitation. Nitrate loads in the hydrologic system of the watershed are primarily derived from human activities (including fertilizer application from agricultural activities and residential wastewater discharges) and the nitrogen cycle. Nitrate loads in surface water declined 16.05 % from 2016 to 2019. Nitrate levels are higher in agricultural farming and residential areas on the eastern and northern sides of the watershed. Additionally, hydrological interactions are usually accompanied by material accumulation and environmental changes. Nitrate levels tend to rise with converging water flows, a process that becomes more pronounced during precipitation events and cropping seasons in agriculturally intensive valley watersheds. However, environmental changes alter nitrogen transformation processes. Nitrogen fixation, nitrification, and ammonification intensify nitrogen inputs during river pooling, enhancing nitrogen cycling fluxes and elevating nitrate loads. These processes are further enhanced during groundwater recharge to surface water, leading to evaluated nitrate load. Enhanced denitrification, dissimilatory nitrate reduction to ammonium (DNRA), anaerobic ammonia oxidation, and assimilation promote the nitrogen export from the system and reduce the nitrate load during surface water recharge to groundwater.

New insights into the controlling factors of nitrate spatiotemporal characteristics in groundwater of Dagu aquifer in Qingdao, China

Identifying spatiotemporal variation of groundwater NO3-N and its primary controlling factors are vital for groundwater protection. This study, under the data scarce conditions and based on time series monitoring data in Dagu aquifer, applied methods including hydrochemical ion ratio, multiple linear regression, support vector regression and grey relational analysis and dedicated to revealing primary controlling factors of temporal variation patterns of groundwater NO3-N. The results showed that agricultural and manure fertilizer are the main sources of NO3-N in north and central area (vegetable farming area), and that domestic sewage discharge and manure fertilizer are the main sources of NO3-N in south area (residential and grain planting area). In addition, results identified the dominant influencing factors of variation of NO3-N in different regions, with human wastewater discharge, nitrogen load amount and water-table depth being the dominant factors of variations of NO3-N in north area, human wastewater discharge being the main factor of variations of NO3-N in central area, and irrigation water and human wastewater being the leading factors of variations of NO3-N in south area. Moreover, types of controlling factors can influence the seasonal variations of NO3-N. NO3-N in vegetable farming area that dominantly affected by fertilization generally shows higher concentration and larger variation range of concentration during summer and autumn than that during spring. NO3-N which mainly affected by human wastewater discharge and manure inputs shows minimal seasonal variation of mean concentration. NO3-N in grain area influenced by irrigation could show more significant variations during spring and autumn than that during summer. The conclusions can enhance understandings of major influencing factors on NO3-N variation in local aquifer. Importantly, the dominant roles of water-table depth and irrigation in NO3-N variation of N2 site (vegetable planting area) and S5 site (grain planting area), respectively, were highlighted.

An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers

Accurately estimating high-dimensional permeability (k) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework. An ensemble MIMR-optimized method is developed to provide more comprehensive monitoring information and avoid missing key information due to the randomness of stochastic response datasets in entropy analysis. A numerical case of solute transport with log-Gaussian permeability fields is presented, with twelve scenarios designed by combining different measurement types and monitoring strategies. The results demonstrated that the proposed ensemble MIMR-optimized method significantly improved the k-field estimates compared to the conventional MIMR method. Additionally, high prediction accuracy in forward modeling is essential for ensuring reliable inversion results, especially for observation data with strong nonlinearity. The findings of this study enhance our understanding and management of k-field estimation in heterogeneous aquifers, contributing to the development of more robust inversion frameworks for general data assimilation tasks.

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.

Numerical modeling of geological sequestration of brine wastewater due to coal mining in the Ordos Basin, China

The coal resources play an indispensable role in the development of heavy industry in China, and coal mining activity leads to brine wastewater drainage, causing major risks for the aquatic environmental system. Thus, the effective and economic treatment of coal mine wastewater is vital to mitigate the environmental burdens, and geological sequestration by deep-well injection is a promising treatment technique. This study elucidates the physical and geochemical processes of coal mine wastewater transport in deep reservoirs and proposes an optimized injection scheme to satisfy environmental and economic benefits simultaneously in the Ordos Basin, China. First, a variable density and variable parameter groundwater reactive transport model is constructed to simulate the long-term process of deep-well injection for coal mine wastewater treatment. Then, the environmental metrics, i.e., the percentage of permeability reduction, the total mass and spatial second moment of the wastewater plume, and the economic metric defined as achieving a higher concentration at a higher injection rate are proposed to evaluate the performance of the injection scheme. The simulation results show that the secondary mineral anhydrite dominates the reduction of reservoir permeability due to the precipitation reactions with SO42- in the brine wastewater, and the permeability in the reaction zone decreases by 0.66 % ~ 1.26 % after 10 years in the basic scenario. Moreover, higher concentrations negatively affect reservoir permeability and increase total dissolved solids, while higher injection rates decrease reservoir permeability and increase the brine wastewater plume. The study also identifies promising schemes that can achieve an optimal trade-off between the conflicting metrics. Based on the economic and environmental benefits demanded in this study, an injection scenario with a concentration of C4 and an injection volume of 800 m3/d is recommended to maximize environmental benefits. Overall, this numerical study offers significant implications for designing an economically and environmentally sustainable treatment injection scheme for coal mining wastewater drainage.

Development of an assessment-based planting structure optimization model for mitigating agricultural greenhouse gas emissions

Optimization of crop structure is an efficient way to reduce greenhouse gas (GHGs) from agriculture production. However, carbon footprint have rarely been incorporated into previous planting structure optimization models due to the challenges of assessing the spatial and temporal distribution of agricultural carbon footprint for multiple crops in irrigated districts. In addition, previous planting structure models suffered from strong subjectivity in objective function determination, and the obtained non-dominated solution set offered difficulties to decision-makers in selecting specific implementation options. To fill such gaps, an integrated accounting-assessment-optimization-decision making (AAODM) approach was proposed, which remedies the shortcomings of previous crop planting structure optimization models in carbon footprint mitigation, and overcomes the subjectivity of objective function determination and the difficulty in selecting specific implementation options. Firstly, life cycle assessment (LCA) method was used to account for the multi-year agricultural carbon footprints of multiple crops in the irrigation district. The optimization objective functions of planting structure optimization models can then be determined based on the assessment method of carbon footprint influencing factors. Next, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to generate a non-dominated solution set of the optimization model. The optimal planting structure can be finally obtained based on decision making methods by determining the maximum harmonic mean (HM) and knee points (KPs) of the non-dominated solution set. The developed AAODM approach was then applied to a case study of agricultural crop management in Bayan Nur City, China. The results showed that the level of economic development was a key factor influencing the increase in carbon footprint in Bayan Nur City over the past 20 years. The regulation of the level of economic development would significantly influence the agricultural carbon footprint in Bayan Nur City. Moreover, two optimal crop cultivation patterns were provided for decision-makers by selecting solutions from the Pareto front with decision making methods. The comparison results with other methods showed that the solutions obtained by NSGA-II were superior to MOPSO in terms of carbon reduction. The developed AAODM approach for agricultural GHG mitigation could help agricultural production systems in achieving low carbon emissions and high efficiency.

Incorporating receiving waters responses into the framework of spatial optimization of LID-BMPs in plain river network region

Deep insights into the receiving waters responses to optimal spatial allocation of LID-BMPs are considered extremely important. This study addressed the urgent need to incorporate receiving waters responses into the spatial allocation optimization of LID-BMPs and demonstrated the efficiency of the approach to guide watershed management. The integration of an overland-river coupling model and the NSGA-III algorithm resulted in the proposal of a general simulation-optimization framework for the optimal layout of LID-BMPs. The coupled model was swapped out for the surrogates to increase computational efficiency. When 40.71%, 36.06%, and 61.80% reductions in runoff volume, flood volume, and TP concentration are achieved, the newly proposed framework can save 34.44% and 16.31% cost compared to the approach that does not consider receiving waters responses and refined spatial allocation, respectively. Results indicate that the incorporation of receiving waters responses and refined spatial allocation are essential for the optimal design of LID-BMPs. This new framework offers the potential for more cost-effective high-cost solutions. The results of spatial optimization are significantly influenced by imperviousness.

Study on Sustainable Agricultural Structure Optimization Method Based on Multiobjective Optimization Algorithm

Agricultural sustainable development is one of the themes of human and nature harmonious coexistence. Adjusting and optimizing agricultural structure are an important direction to improve the level of agricultural sustainable development. In this paper, related research status of the sustainable development of agriculture is analyzed; it shows that there is lack of scientific theories guidance for agricultural sustainable development. In order to optimize sustainable development of agriculture industry structure, the guidance theory of and its optimization are studied. Based on multiobjective optimization theory, several key factors that affect agricultural sustainable development and the main target indexes of agricultural sustainable development are analyzed, the mathematical model of the evaluation of the sustainable development of agriculture is established, and the solution to optimize the multiobjective model is studied. Finally, the agricultural industry sustainable development in a certain area is taken as the research object in this paper; the mathematical model and solving method of agricultural sustainable development evaluation are studied; it provides a guidance to optimize the regional agricultural industrial structure and improve the quality of agricultural sustainable development.