Long-term assessment on performance and seasonal optimal operation of a full-scale integrated multiple constructed wetland-pond system.

Constructed wetlands as natural process-based water treatment technologies are popular globally. However, lack of detailed long-term assessment on the impact of seasonal variations on their performance with focus on optimal seasonal adjustments of controllable operating parameters significantly limits their efficient and sustainable long-term operation. To address this, a full-scale integrated multiple surface flow constructed wetlands-pond system situated between slightly polluted river water and outflow-receiving waterworks in a subtropical monsoon climate area of middle-eastern China was seasonally assessed over a period of six years. During this period, the removal rate (R) and mass removal rate (MRR) of total nitrogen (TN), total phosphorus (TP) and chemical oxygen demand (COD) possessed strong seasonality (p < 0.05). The highest R (%) and MRR (mg/m2/d) were in summer for TN (51.53 %, 114.35), COD (16.30 %, 143.85) and TP (62.39 %, 23.89) and least in spring for TN (23.88 %, 39.36) and COD. Whereas for TP, the least R was in autumn (37.82 %) and least MRR was in winter (9.35). Applying a first-order kinetics model coupled with Spearman's rank correlation analysis, purification efficiency exhibited significant dependence on temperature as nutrient reaction rates constant, k generally increased with temperature and was highest in summer. Meanwhile, the R of TN, TP and COD were positively correlated with influent concentration whiles MRR of TP was negatively correlated with hydraulic retention time but positively correlated with hydraulic loading rate (HLR) (p < 0.05). Also, MRR of COD and TN were positively correlated with mass loading rates (MLR) in summer and autumn. Through linear optimization, the best operating parameters according to the compliance rate were determined and a set of guidelines were proposed to determine the optimal operational change of hydrological index in each season (Spring, 0.1-0.12 m/d; Summer, 0.14-0.16 m/d; Autumn, 0.15-0.17 m/d; Winter, 0.1-0.11 m/d) for efficient and sustainable long-term operation.

A radial basis function neural network based multi-objective optimization for simultaneously enhanced nitrogen and phosphorus removal in a full-scale integrated surface flow treatment wetland-pond system.

In this study, a radial basis function neural network (RBFNN) model was developed and implemented in a multi-objective optimization procedure to determine the optimal hydraulic loading rate (HLR), hydraulic retention time (HRT), and mass loading rates (MLR) for enhanced removal of nitrogen and phosphorus by an integrated surface flow treatment wetland-pond system treating drinking source water in Yancheng, China. Prior to modelling, the system's 6-year nitrogen and phosphorus removal efficiencies were found to trend downwards as effluent concentrations trended positively. Meanwhile, operating parameter interaction effects impacted final effluent quality. Thus, total nitrogen and total phosphorus removal were simulated by an RBFNN model with satisfactory R2 of 0.99 and 0.98 respectively. Optimal average HLR, HRT and MLR for 80% simultaneous removal efficiencies were subsequently determined to be 0.10860 ± 0.03 md-1, 30.43 ± 9.96 d and 306.416 ± 89.54 mgm-2d-1 respectively. The results highlight the feasibility of the RBFNN modelling based optimization procedure for treatment wetlands.

Nitrate repletion during spring bloom intensifies phytoplankton iron demand in Yangtze River tributary, China.

Most aquatic systems show characteristic seasonal fluctuations in the total nutrient pool supporting primary productivity. The nutrient dynamics essentially exacerbate critical demand for the counterpart micronutrients towards achieving ecosystem equilibrium. Herein, the phytoplankton demand for iron (Fe) uptake under high concentration of nitrate-nitrogen during spring in Xiangxi Bay, China, was studied. Our result confirmed that significant Fe concentrations (P = 0.01) in both autumn (0.62 ± 0.02 mgL-1) and winter (0.06 ± 0.03 mgL-1) relative to spring (0.004 ± 0.01 mgL-1) are linked to the low NO3-N paradigms during autumn and winter. As NO3-N showed a sharp increase in spring, a dramatic reduction in the Fe pool was observed in the entire tributary, driving the system to a critical Fe limited condition. Bioassay study involving Fe additions both alone and in combinations led to maximum growth stimulation with biomass as chla (16.44 ± 0.82 µgL-1) and phytoplankton cell density (6.75 × 106 cellsL-1) which differed significantly (P = 0.03) with the control. Further, the study demonstrated that Fe additions triggered biomass productions which increased linearly with cell densities. The P alone addition caused biomass production (15.26 ± 2.51 µgL-1) greater than both NO3-N (9.15 ± 0.66 µgL-1) and NH4+N (13.65 ± 1.68 µgL-1) separate additions but reported a low aggregate cell density (3.18 × 106 cellsL-1). This indicates that nutrient and taxonomic characteristics e.g., high cell pigment contents rather than just the cell bio-volume also determine biomass. The Bacilliarophyta, Chlorophyta, and Cryptophyta with the total extinction of Cyanophyta characterized the bloom in spring. The anthropogenic NO3-N input into XXB would have driven to higher NO3-N than NH4+N situation, and incapacitated the Cyanophyta that preferentially utilize NH4+N. Our study provides a useful report for incorporation into the monitoring programs for prudent management of phytoplankton bloom and pollution across the eutrophic systems.