Agricultural activities and climate change drive nutrient cycling in agriculture-dominated lakes, thereby inducing dynamic migration of non-point source pollution (NPSP). Thus, simulating NPSP variation driven by agricultural activities and climatic factors attracts substantial attention. This study developed a novel dual-layer stacking model to improve NPSP simulation accuracy by leveraging multiple heterogeneous base models to capture diverse feature patterns and optimizing their integration via a meta-learner. The model identified the area-specific dominant drivers in agriculture-dominated lakes through feature importance ranking. The effectiveness and practicality were verified at Hongze Lake, the fourth-largest freshwater lake in China. Based on 20 years of observational data, the DLS model demonstrated satisfactory simulation performance. The coefficient of determination (R2) values for total phosphorus (TP) and ammonia nitrogen (NH3-N) were 0.831 and 0.829 in the inflow area, and 0.823 and 0.811 in the outflow area, respectively. It was found that fertilizer application rate (FR) ranked among the top three features in both inflow and outflow areas, highlighting the key role of agricultural activities in regulating NPSP. The synergy between fertilization and temperature was the predominant driver of NPSP in the outflow area, as high temperature promotes both the endogenous nutrient release from sediments and the exogenous nutrient input from fertilizers. Dissolved oxygen (DO) was the predominant driver of TP in the inflow area, likely through its control on iron-phosphorus fixation. This study provides a recommended methodology to enhance the simulation accuracy of NPSP and represented a new exploration of spatially differentiated management of NPSP in agriculture-dominated lakes.

Hydrodynamic models of river networks are commonly used for flood disaster simulation, and the accuracy of model parameter settings directly affects the reliability of simulation results. Among these, Manning's roughness coefficient is the core parameter for calibrating one-dimensional(1D) hydrodynamic models, as it is the most sensitive and frequently adjusted parameter. Taking the Yunxi Area of Huai'an City as a case study, this paper proposes an integrated workflow using orthogonal experiments and successive approximation for calibrating Manning's roughness coefficients in river networks. In this workflow, 13 river reaches (from six major rivers) serve as experimental factors. The Manning's roughness coefficients for the main channel and floodplains are assigned different values as experimental levels. Model performance is evaluated using the Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). A multi-factor and multi-level orthogonal table L27(313) of main channel or floodplains roughness is alternately selected to design 27 sets of experiments. Through HEC-RAS simulation and orthogonal analysis, the roughness coefficients of the main channel and floodplains are alternately screened and successively approximated to the target values. Finally, the roughness coefficients of the main channel and floodplains for each river reach meeting the accuracy requirements are obtained, with corresponding values of NSE = 0.93 and RMSE = 0.04 m. The results show that orthogonal experimental design significantly reduces the number of simulation tests and effectively saves computational time and costs, while the successive approximation strategy addresses the complexity of solving problems with multiple decision variables. Additionally, the experimental factors consider variations in cross-section types and hydraulic conditions along the river by setting roughness coefficients in segments. The orthogonal experimental design ensures the relevance, simultaneity, and systematic nature of parameter adjustments across all river reaches, significantly enhancing the rationality and reliability of the model parameter calibration.

To address the challenge of optimizing hydrological parameters for nitrogen pollution control in paddy polders, this study coupled the Stella eco-dynamics model with an external optimization algorithm and developed a nonlinear programming framework using the water surface ratio and inflow rate as decision variables and the maximum nitrogen removal rate as the objective function. The simulation and optimization conducted for the Hongze Lake polder area indicated that the model exhibited strong robustness, as verified through Monte Carlo uncertainty analysis, with coefficients of variation (CV) of nitrogen outlet concentrations all below 3%. Under the optimal regulation scheme, the maximum nitrogen removal rates (eta 1, eta 2, and eta 4) during the soaking, tillering, and grain-filling periods reached 98.86%, 98.74%, and 96.26%, respectively. The corresponding optimal inflow rates (Q*) were aligned with the lower threshold limits of each growth period (1.20, 0.80, and 0.50 m3/s). The optimal channel water surface ratios (A1*) were 3.81%, 3.51%, and 3.34%, respectively, while the optimal pond water surface ratios (A2*) were 19.94%, 16.30%, and 17.54%, respectively. Owing to the agronomic conflict between "water retention without drainage" and concentrated fertilization during the heading period, the maximum nitrogen removal rate (eta 3) during this stage was only 37.34%. The optimal channel water surface ratio (A1*) was 2.37%, the pond water surface ratio (A2*) was 19.04%, and the outlet total nitrogen load increased to 8.39 mg/L. Morphological analysis demonstrated that nitrate nitrogen and organic nitrogen dominated the outlet water body. The "simulation-optimization" coupled framework established in this study can provides quantifiable decision-making tools and methodological support for the precise control and sustainable management of agricultural non-point source pollution in the floodplain area.

Water and croplands are spatiotemporally heterogeneous in agricultural production and their sustainable use is important for supporting water-economy-environment cycles. This study proposes a fuzzy multi-objective programming model based on water footprint (WF) theory to achieve a universally optimal land-water allocation strategy under uncertainties. The model balances the trade-offs among water-saving, economic benefits, and environmental benefits while ensuring food security and addressing uncertainties from climate, agricultural production, and market fluctuations. Subsequently, a tri-intuitionistic fuzzy decomposition simplex aggregation algorithm is proposed to handle these uncertainties and generate the optimal spatial cropping patterns via integrating fuzzification, defuzzification, decomposition, aggregation, and simplicity mechanisms. The applicability and effectiveness of the methodology were validated in Jiangsu Province, China. Results indicated cotton had the highest total WF (4454 f 480 m3/ton) among six crops based on estimations obtained from daily climate observations conducted from 2002 to 2022 in 21 water function zones of Jiangsu, followed by rape (1907 f 152 m3/ton), wheat (1223 f 89 m3/ton), rice (899 f 70 m3/ton), peanut (1080 f 124 m3/ton) and maize (780 f 78 m3/ton). Optimal results from the Pareto front demonstrated improvements of 6.3 %, 2.1 %, and 3.2 % in water-saving, economic benefit, and environmental benefits objectives, respectively, compared to the actual scenario. The optimal cropping pattern suggested increasing the proportion of crops with high profit and low fertilizer (i.e., rape) during the dry season, and crops with low irrigation dependency (i.e., maize) during the rainy season. This study provides a scientific methodology and guidelines for decision-makers to balance the trade-offs in water-economy-environment cycles in agricultural sustainability.

Frequent changes in the hydrological regimes lead to divergent responses of water, food, and carbon (C) emissions in agricultural production, which challenge sustainable agricultural development. Therefore, this study proposed a systematic multi-objective non-linear programming model for investigating divergent responses of optimal land and water allocation to different hydrological regimes from the perspective of the agricultural water-food-carbon (AWFC) nexus framework. The model was capable of simultaneously tackling the trade-offs among water consumption, economic benefit, and C emissions by integrating with spatial-temporal water footprint (WF) and carbon footprint (CF) theories. The universal cropping patterns in each spatial water function zone that adapted to the spatiotemporal variations of hydrological regimes could also be obtained. The applicability and effectiveness of the proposed methodology were verified by a real-world, provincial-scale case, i.e., Jiangsu Province, southeast China. As for the actual scenario, the optimal scheme under normal, wet, and dry years highlighted the significance of improvement in water-saving (10.31%-12.26%), while showing a slight increase in net economic benefit (2.68%-2.85%) and low-carbon agricultural competitiveness (1.21%-3.58%). It was found that the wet year performed the greatest water-saving potential, and the dry year showed the strongest low-carbon agricultural competitiveness. The optimal cropping patterns suggested that it was a promising management strategy to enhance the comprehensive benefits related to the economy, water, and carbon by increasing the planting proportion of high-value crops with low WF and CF. This study provided scientific methodology and instructions for balancing the trade-off among economy, water, and environment components in sustainable agricultural development.

Black bloom is a very serious water pollution phenomenon in eutrophic lakes, with Fe(II) and S(−II) being the key limiting factors for this problem. In this paper, three different machine learning methods, namely, Random Forest (RF), Gaussian Mixture Model (GMM), and Bayesian Network (BN), were used to explore the complex interactions among Fe(II), S(−II), and other aquatic factors in the estuary of Chaohu Lake to better characterize and monitor water degradation by black bloom. The results of RF showed that total nitrogen (TN), ammonia, total phosphorous (TP), suspended sediment concentration (SSC), and oxidation–reduction potential (ORP), which were chosen from 11 factors, had the most important relationships with Fe(II) and S(−II). The 69 sampling sites were divided in three groups identified as worst, worse, and bad according to the observed values of seven factors using the GMM. Then, the BN model was applied to three observation groups. The results showed that the structures of the interaction networks were different between the groups. S(−II) controlled only SSC production in the bad and worse group sites, while SSC was determined by both S(−II) and Fe(II) in the worst group. Ammonia and TN exhibited the most direct importance for S(−II) and Fe(II) production in all observation groups. According to the indications from the BNs, potential management strategies for different water pollution conditions were developed. Finally, the threshold values of Fe(II), S(−II), TP, ammonia, TN, SSC, and ORP, which were 0.80 mg/L, 0.04 mg/L, 0.45 mg/L, 3.44 mg/L, 4.15 mg/L, 55 mg/L, and 135 mv, respectively, were determined on the basis of the BN models. These values will be helpful to develop accurate strategies of oxygenation to quickly eliminate black bloom in the lake.

The frequency and intensity of urban flooding continuously increase due to the dual influences of climate change and urbanization. Conducting individual importance classification of urban stormwater channel networks (USCNs) is of significant importance for alleviating urban flooding and facilitating targeted stormwater management implementation. However, a quantitative classification method is lacking for trellis networks, which are a common type of USCN. This study proposed a novel importance classification methodology for channel segments in most types of USCNs, especially suitable for trellis networks, based on permutation and algebraic graph theory. The concept of permutation was integrated into the methodology to measure the importance of each channel segment to the USCN. Algebraic graph theory was employed to quantify the topological structure and hydraulic characteristics of the USCN. To verify the applicability and rationality of the proposed methodology, a real-world city with trellis USCNs in China (i.e., Huai’an) was selected as the study area. Seventy channel segments in the USCN were efficiently classified into three categories based on individual importance. This study provided a decision-support methodology from the perspective of individual importance classification in the USCN and offered valuable reference for urban flooding managers.

The coupling partial denitrification and anammox (PD/A) is a promising technology for deep-level nitrogen removal, and the quickly effective startup remains a huge challenge in real application. In this study, the stability of nitrite (NO2--N) accumulation was investigated in the PD sequential batch reactor (PSBR), and the nitrogen removal performance in response to nitrogen loading rate (NLR) was compared in another independent anammox SBR (ASBR), where the combined PD/A operation was subsequently carried out based on the optimal operating conditions. In the PSBR, nitrate removal efficiency (NRE) maintained around 89% - 90% with a mean nitrate-to-nitrite transformation ratio (NTR) of 84.43%, and the total proportion of functional bacteria (e.g., Terrimonas, norank_f_A4b, Ellin6067, Defluviicoccus, etc) accounted for 40.98% after long-term acclimation. In the ASBR, increased NLR (0.22 → 0.70 kgN/(m3·d)) depressed the anammox activity owing to the sludge disintegration and adverse nitrogen contribution, however, the dominant Candidatus_Brocadia enriched from 0.50% to 2.05% and realized the efficient retention of functional bacteria, which also laid a good foundation for the rapid startup of PD/A process. As NLR controlled around 0.18 - 0.25 kgN/(m3·d), the total nitrogen removal (ReTN) climbed from 33.88% to 98% with anammox contribution soaring up to 65%, since the higher tight-bound extracellular polymeric substances (T-EPS) production and protein/polysaccharide (PN/PS) ratio accelerated the re-formation of anammox granular sludge. In the end, the potential significances and challenges of PD/A process were further summarized for the mainstream wastewater treatment.

China experiences frequent heavy rainfall and flooding events, which have particularly increased in recent years. As flood storage zones (FDZs) play an important role in reducing disaster losses, their ecological restoration has been receiving widespread attention. Hongze Lake is an important flood discharge area in the Huaihe River Basin of China. Previous studies have preliminarily analyzed the protection of vegetation zones in the FDZ of this lake, but the future growth trend of typical vegetation in the area has not been considered as a basis for the precise protection of vegetation diversity and introductory cultivation of suitable species in the area. Taking the FDZ of Hongze Lake as an example, this study investigated the change trend of the suitability of typical vegetation species in the Hongze Lake FDZ based on future climate change and the distribution pattern of the suitable areas. To this end, the distribution of potentially suitable habitats of 20 typical vegetation species in the 2040s was predicted under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate scenarios using the latest Coupled Model Intercomparison Project CMIP6. The predicted distribution was compared with the current distribution of potentially suitable habitats. The results showed that the model integrating high-performance random forest, generalized linear model, boosted tree model, flexible discriminant analysis model, and generalized additive model had significantly higher TSS and AUC values than the individual models, and could effectively improve model accuracy. The high sensitivity of these 20 typical vegetation species to temperature and rainfall related factors reflects the climatic characteristics of the study area at the junction of subtropical monsoon climate and temperate monsoon climate. Under future climate scenarios, with reference to the current scenario of the 20 typical species, the suitability for Nelumbo nucifera Gaertn decreased, that for Iris pseudacorus L. increased in the western part of the study area but decreased in the eastern wetland and floodplain, and the suitability of the remaining 18 species increased. This study identified the trend of potential suitable habitat distribution and the shift in the suitability of various typical vegetation species in the floodplain of Hongze Lake. The findings are important for the future enhancement of vegetation habitat conservation and suitable planting in the study area, and have implications for the restoration and conservation of vegetation diversity in most typical floodplain areas.

Agricultural production consumes the majority of global freshwater resources. The worsening water scarcity has imposed significant stress on agricultural production when regions seek food self-sufficiency. To seek optimal allocation of spatial agricultural water and land resources in each water function zone of the objective region, a multi-objective optimization model was developed to tackle the trade-offs between the water-saving objective and the economic benefit objective considering virtual water trade (VWT). The cultivated area of each crop in each water function zone was taken into account as the decision variable, while a set of strong constraints were used to restrict land resources and water availability. Then, a decomposition-simplex method aggregation algorithm (DSMA) was proposed to solve this nonlinear, bounding-constrained, and multi-objective optimization model. Based on the quantitative analysis of the spatial blue and green virtual water in each agricultural product, the proposed methodology was applied to a real-world, provincial-scale region in China (i.e., Jiangsu Province). The optimized results provided 18 Pareto solutions to reallocate the land resources in the 21 IV-level water function zones of Jiangsu Province, considering four major rainy-season crops and two dry-season crops. Compared to the actual scenario, the superior scheme increased by 7.95% (5.6 × 109 RMB) for economic trade and decreased by 1.77% (2.0 × 109 m3) for agricultural water consumption. It was mainly because the potential of spatial blue and green virtual water in Jiangsu was fully exploited by improving spatial land resource allocation. The food security of Jiangsu could be guaranteed by achieving self-sufficiency in the superior scheme, and the total VWT in the optimal scheme was 2.2 times more than the actual scenario. The results provided a systematic decision-support methodology from the perspective of spatial virtual water coordination, yet, the methodology is widely applicable.

The effective regulation and storage of water resources by reservoirs in arid and semi-arid areas is important for alleviating water resource shortages. In this paper, multiple irrigation reservoirs and pumping stations are evaluated, according to their special hydraulic connections and used to establish the water resources optimal allocation model of the parallel ‘reservoir and pumping station’ irrigation system. The mathematical model takes the maximum total income of the whole irrigation area as the objective function, the water supply and spill of the reservoir and the water replenishment of the pumping station as the decision variables, and the system water supply, agricultural water rights, reservoir operation criteria as the constraints. A new multilevel decomposition aggregation dynamic programming (MDADP) algorithm is proposed to solve the complex nonlinear model and is compared with the real-coded genetic algorithm, particle swarm optimization, cat swarm optimization and whale optimization algorithm. From the analysis of optimality and the applicability of the algorithm, MDADP was found to be more suitable than the above heuristic models for solving problems in the field of the optimal allocation of water resources.

In humid regions with the monsoon climate, seasonal water shortages and water spills occur alternately because of uneven temporal and spatial distributions of water resources. An optimization model for the in-series reservoir (ISR) with replenishment pumping stations was developed to obtain the minimum annual sum of water shortage and systematically considered the reservoir operation rule of water spill and replenishment. This model features multiple dimensions; dynamic programming (DP) may cause a 'curse of dimensions', while the decomposition-coordination method has difficulty in judging logic conditions in the reservoir operation rules. So, an improved decomposition and DP aggregation (DDPA) method was proposed. The proposed model and the method were applied to a real case in the humid region of southern China. Compared to a conventional scheduling method, the water supply was increased by 0.8% and replenishment was reduced by 2.5%. Moreover, a comparison between DDPA and six heuristic algorithms was discussed. All heuristic algorithms' objective function values only obtained local optimal solutions, and the water shortage of the system was 0.12-20.5%. The obtained results demonstrated that DDPA was the better choice for highly complex multi-reservoir systems. The proposed optimization algorithm of this study enriched the optimization theory of multi-dimensional and multi-variable complex systems.

Ensuring an optimal irrigation system and planting layout for crops in areas with water resource deficiencies is a complex process. A model of the optimal allocation of water and land resources for the irrigation system of the ‘reservoir and pumping station’ under crop rotation was established in this study. For the above complex nonlinear model, two-hybrid algorithms are proposed: (1) the decomposition aggregation dynamic programming (DADP) method and linear programming (LP) successive approximation algorithm [(DADP–LP)SA] and (2) the DADP algorithm based on the orthogonal design (OD) method (OD–DADP). The (DADP–LP)SA and OD–DADP algorithms were compared with the real-coded genetic algorithm (RGA) and particle swarm optimization (PSO) to analyze the performance of the four algorithms. The developed algorithms were applied to the Gao'a irrigation area in the north of Jiangsu Province, China. The solution results showed that the annual output value of water-deficient irrigation areas was improved, and limited water and land resources were optimally allocated, demonstrating the feasibility of the two-hybrid algorithm. Moreover, through a comparative analysis of the optimality and applicability of the four algorithms, it can be observed that (DADP–LP)SA and OD–DADP are more suitable for optimizing the allocation of scarce water and land resources than RGA and PSO.

【Objective】The optimal scheduling method of "two reservoirs and three pumping stations" irrigation system in hilly areas was studied.【Method】Aiming at the problem of joint operation of two reservoirs and three pumping stations in drought years in hilly areas， the optimal scheduling model of "two reservoirs and three pumping stations" irrigation system was constructed. the objective function was to take the minimum sum of squares of irrigation water shortages in each irrigation area and each period， and the constraints included the annual water supply of the system， the annual water supply of the pumping station， the water lifting capacity of the pumping station， and the optimal scheduling criteria of the reservoir， etc. The large system decomposition-coordination method based on experimental optimization was used to solve the optimal scheduling scheme.【Result】Taking the joint optimal operation of Shanhu Reservoir and Niqiao Reservoir in Liuhe District of Nanjing City， Jiangsu Province in dry years as an example， the result showed that compared with the conventional scheduling scheme of preferred parameters， the irrigation water shortage in Niqiao Irrigation District was reduced from 350，000 m3 to 00，000 m3， the amount of abandoned water in Shanhu Reservoir was reduced from 450，000 m3 to 00，000 m3， the amount of extracted river water was increased from 4.03 million m3 to 4.35 million m3， and the terminated water storage of Shanhu Reservoir and Niqiao Reservoir was increased from 8.19 million and 1.45 million m3 to 8.47 million and 1.59 million m3 of initial water storage.【Conclusion】The large system decomposition-coordination method based on experimental optimization is used to solve the optimal scheduling scheme， which can effectively reduce the amount of abandoned water in the reservoir and increase the amount of irrigation water supply. It can also give full play to the water lifting capacity of the pumping station and reduce the number of water lifting of the pumping station. In addition， it can also meet the requirements for the specific value of the terminal water storage of the reservoir.

采用厌氧/缺氧/好氧-生物接触氧化(A2/O-BCO)工艺处理低碳氮(C/N)比污水,考察单因素碳源(阶段Ⅰ:乙酸钠;阶段Ⅱ:乙酸钠+丙酸钠;阶段Ⅲ:丙酸钠)对有机物去除以及同步脱氮除磷的影响,并重点探究乙酸钠、丙酸钠混合碳源条件下内碳源(PHA、Gly)的转化利用以及反硝化除磷(DPR)机理,同时通过高通量测序对比了不同阶段微生物菌群结构的演变规律.结果表明:混合碳源提高了有机物、氮、磷的同步去除效率,厌氧段内碳源转化量为226mg/h,释磷量高达30.58mg/L,DPR效率稳定在90％以上;批次试验表明反硝化聚磷菌(DPAOs)占聚磷菌(PAOs)的比例为72.42％,基本实现了DPAOs的富集;高通量测序结果表明混合碳源更有利于形成独特的OTUs菌群,PAOs(包括Accumulibacter和Acinetobacter)和DPAOs(包括Dechloromonas和Pseudomonas)总量高达29.13％(＞16.18％(阶段111)＞14.34％(阶段Ⅰ)),有效促进了碳源的高效利用以及反硝化除磷效率;BCO反应器中氨氧化菌(AOB,包括Nitrosomonas和Nitrosomonadaceae)和亚硝酸盐氧化菌(NOB,以Nitrospira为主)总量从3.89％(N1)增加到23.09％(N2)、37.23％(N3),为反硝化除磷提供充足的电子受体;此外,建立了基于碳源高效利用的运行调控策略,以期为A2/O-BCO工艺的推广应用提供理论参考.

Aiming at the optimal allocation of irrigation water in multi-water source project in water resource shortage area, this study developed a water resource joint scheduling optimization model for the reservoir and the pumping station under deficit irrigation conditions. In the model, the maximum annual yield of the irrigation area was the objective function; the water supply, water spill of the reservoir and replenishment water of the pump station at each stage as the decision variables; and the total annual water supply of the system, the reservoir operation criteria, the water rights of the pump station, and the water demand of the crop during the entire growth period were the constraint conditions. According to the characteristics of the model, a large system decomposition aggregation dynamic programming (DADP) method is proposed to transform N + 1 dimensional dynamic programming problem into N + 1 one-dimensional dynamic programming problem for solution. In addition, this study also uses real-coded genetic algorithm (RGA) and DADP to compare the algorithms, and discusses the performance of the two algorithms from the optimization of the algorithm and the applicability of the algorithm.

针对农业圩区的排涝以及水环境问题,该研究以江苏里下河地区为研究对象,以满足里下河圩区设计排涝标准与水质净化要求为目标,构建农业圩区坑塘-排水沟道湿地系统最优水面率数学模型.模型以工程系统总费用现值最小为目标函数,以泵站涝水外排能力与圩内水面率、水面率上下限、水环境容量与圩内坑塘与排水沟道系统的关系(主要包括圩内坑塘沟道湿地系统对总氮、总磷以及铬的化学需氧量的去除率)等为约束条件,以圩内坑塘(湖泊)水面率、排水沟道水面率、外排涝水泵站设计排涝流量为决策变量,采用遗传算法对模型进行求解.对江苏里下河地区阜宁县渠南灌区的圩区河湖与排水工程系统进行实例优化分析可知:当采用明沟排水系统,圩内总水面率为11.35％(其中坑塘、排水沟道系统水面率分别为8.15％、3.20％)、设计排涝模数为0.86 m3/(s·km2)时,工程建设费用现值最小;此时,圩区可达到20年一遇设计排涝标准、且圩外周边水体为V类时,圩内水体可达到IV类水标准.该方法可为同类地区在国土整治、防洪排涝规划、河湖水体净化等提供参考.

A three-stage plug flow moving bed biofilm reactor (PF -MBBR, consisting of three identical chambers of N1, N2 and N3) was proposed for nitrifier enrichment using synthetic wastewater. During the stable operation, the average NH4(+)- N effluent was 0.67 mg/L and NH4(+)-N removal was as high as 97.19% with the nitrite accumulation ratio (NAR) of 54.23%, although the biofilm thickness and biomass both presented downward trends from N1 (296 mu m, 2280 mg/L), N2 (248 mu m, 1850 mg/L) to N3 (198 mu m, 1545 mg/L). Particularly, the comparative results of three stages revealed that N2 showed the optimum NH4(+)-N removal (77.27%) and NAR (75.21%) in the continuous-flow, while NAR of N3 unexpectedly maintained a high level of 65.83% in the batch test, suggesting that ammonia oxidizing bacteria (AOB) accounted for absolute advantage over nitrite oxidizing bacteria (NOB). High-throughput sequencing initially verified different distribution of bacterial community structure, where N2 was far away from N1 and N3 with the lowest community richness and community diversity (operational taxonomic units (OTUs): 454(N2)< 527(N3)< 621(N1)). Proteobacteria (77.60%-83.09%), Bacteroidetes (1.66%-3.66%), Acidobacteria (2.28%-4.67%), and Planctomycetes (1.19%-6.63%) were the major phyla. At the genus level, AOB (mainly Nitrosomonas) accounted for 5.08% (N1), 20.74% (N2) and 14.24% (N3) while NOB (mainlyNitrospira) increased from 0.14% (N1), 7.06% (N2) to 4.91% (N3) with the total percentages of 5.22%, 27.80% and 19.15%. Finally, the application feasibility of MBBR optimization linked with nitrite (NO2--N) accumulation for deep-level nutrient removal was discussed.

城镇环状给水管网的优化设计对于降低整体工程造价起到重要作用.以管网投资总费用最小为目标,以各管段管径为决策变量,构建了环状管网的离散非线性优化模型.针对环状管网优化模型中等式约束多、决策变量离散化、极易陷入局部最优等问题,提出了一种耦合水力模拟引擎的布谷鸟算法(CS-EPANET).此外,通过螺旋更新机制,进一步平衡了该算法的全局和局部寻优能力;并通过对该算法参数的动态改进,提升了算法迭代后期的搜索速率和精度.通过Two-loop、Ha-noi 算例和多环供水管网实例计算,验证了该算法的可行性和有效性.

The subtropical monsoon climate zone features abundant water resources but with uneven temporal and spatial distribution, so seasonal water shortages are frequent. In order to reduce the water shortage and water spill in this region, a nonlinear optimization model for the joint operation of a system of a reservoir and two pumping stations is developed in this paper. In this model, the water supply of the reservoir and pumping volume of the pumping stations in each period are two types of decision variables, which are subjected to the annual available water in the reservoir, water rights of the two pumping stations and the operation rule of the reservoir. However, modern intelligent algorithms may fail in dealing with constraints of if-statements like the operation rule of the reservoir in this model. In light of the shortcoming of the classical genetic algorithm, a modified genetic algorithm is proposed by comparing the different methods for dealing with constraints. The modified algorithm shows a better adaptability to the operation rule. The modified genetic algorithm may provide a reference for similar modern intelligent algorithms to solve optimal water resources allocation for systems of multiple reservoirs and multiple pumping stations.