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.

Enhanced soil potential N2O emissions by land-use change are linked to AOB-amoA and nirK gene abundances and denitrifying enzyme activity in subtropics.

Conversion of forestland to intensively managed agricultural land occurs worldwide and can increase soil nitrous oxide (N2O) emissions by altering the transformation processes of nitrogen (N) cycling related microbes and environmental conditions. However, little research has been conducted to assess the relationships between nitrifying and denitrifying functional genes and enzyme activities, the altered soil environment and N2O emissions under forest conversion in subtropical China. Here, we investigated the long-term (two decades) effect of converting natural forests to intensively managed tea (Camellia sinensis L.) plantations on soil potential N2O emissions, inorganic N concentrations, functional gene abundances of nitrifying and denitrifying bacteria, as well as nitrifying and denitrifying enzyme activities in subtropical China. The conversion significantly increased soil potential N2O emissions, which were regulated directly by increased denitrifying enzyme activity (52 %) and nirS + nirK gene abundance (38 %) as shown by structural equation modeling, and indirectly by AOB-amoA gene abundance and inorganic N concentration. Our results indicate that converting natural forests to tea plantations directly increases soil inorganic N concentration, resulting in increases in the abundance of soil nitrifying and denitrifying microorganisms and the associated N2O emissions. These findings are crucial for disentangling the factors that directly and indirectly affect soil potential N2O emissions respond to the conversion of forest to tea plantation.

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.

Evaluation of the long-term performance in a large-scale integrated surface flow constructed wetland-pond system: A case study.

Limited information is available in regards to the long-term treatment performance of large-scale integrated surface flow constructed wetland-pond (ISFWP) system improving drinking water source. This study aimed to investigate the treatment performance of a large-scale ISFWP system for the improvement of drinking water source. During five years of operation, the average effluent water quality in the ISFWP system could comply with Chinese Environmental Quality Standards for Drinking Water Source. The average removal efficiencies of permanganate index (CODMn), ammonia nitrogen, total nitrogen (TN), total phosphorus, and fecal coliforms were 7.6%, 44.3%, 42.9%, 50.8%, and 88.6%, respectively. The treatment performance in the ISFWP system was stable during the operation time, while TN removal efficiency declined by 38.2% after five years of operation. Moreover, contaminants removal efficiencies were not subject to change of season, except for CODMn and TN. Consequently, efficient and sustainable contaminants removal in the large-scale ISFWP system still possessed challenges, especially for CODMn and TN.