Seasonal variations of performance and operation in field-scale storing multipond constructed wetlands for nonpoint source pollution mitigation in a plateau lake basin
Graphical abstract
Introduction
Non-point source (NPS) pollution control is an urgent global challenge in saving energy and resource (Ding et al., 2017, Liu et al., 2018). As the main forms in rural non-point sources, rural domestic wastewater and agricultural runoff containing excessive nitrogenous and phosphorus compounds caused the eutrophication of recipient water bodies (Li et al., 2018, Zhao et al., 2016).
Constructed wetlands (CWs), popularly used in NPS pollution control, are effective applications listed in the best management practices (BMPs) (Wang and Sample, 2014, Wu et al., 2017). Thus, lake or river watershed management through effective and efficient CWs is necessary for mitigating excessive nutrient from NPS pollution before discharged into recipient water (Han et al., 2017). However, the dispersedly temporal and spatial distribution of water quality and quantity from NPS pollution increased the difficulties to obtain sustainable and favorable purification efficiencies of CWs. Rainwater retention ponds are widely adopted in the interception of NPS pollution from rainwater runoff all over the world (Borne, 2014, Yang and Toor, 2017). Therefore, the coupling between ecological multiponds and CWs afforded a novel system to deal with random and changeable NPS pollution, especially heavy rainfall runoff (Huang et al., 2018, Sutherland et al., 2018).
The ecological storing multipond constructed wetlands (SMCWs), which are mimics of natural ponds or wetlands around large rivers or lakes, function as habitats, reduce the NPS pollution loading into rivers or lakes and then improve water quality and water environmental bearing capacity (WEBC) through the storage and interception of wastewater in watershed. With low investment cost, convenient management, large storage capacity, and environmental-friendly ecological restoration, SMCWs play an important role in water environmental health and ecological restoration of watershed (Ilyas and Masih, 2017, Maucieri et al., 2017, Yin and Shan, 2001).
Most significant parameters, including hydraulic loading rate (HLR), hydraulic retention time (HRT), mass loading rate (MLR), vegetation, water depth, temperature and season, were proved to have impacts on the performances of CWs (Guo et al., 2017, Wu et al., 2015). Additionally, the calibration of traditional empirical parameters was highly related to small-scale and pilot-scale experiments (Singh et al., 2017, Sutherland et al., 2014, Tao et al., 2017). However, results were usually unsatisfactory in the practical application of engineering in field-scale experiment due to some uncontrollable factors, such as amplification effects, geography, climate, rainfall, season, etc. Under such circumstances, apprehension of considering CWs as an alternative treatment for NPS pollution control is not surprising (Chen et al., 2011). Up to now, few attentions have been paid to either the influences of seasonal variations of operational parameters (MLR, water depth, HLR and HRT) on the performance of field-scale CWs or the optimization of parameters (Sutherland et al., 2014). To gain better performances of CWs, suitable parameters should be designed in the engineering application in typical basin for the establishment of appropriate empirical parameters and the optimization of parameters.
This study focuses on the performance and operation of filed-scale SMCWs for the treatment of NPS pollution in plateau Erhai Lake basin, aiming to fill the knowledge gap of optimization and calibration of operation parameters for engineering application in the watershed management of plateau lake. Specific objectives are to (1) assess the characteristics of seasonal variations of parameters and nutrient removal by SMCWs; (2) analyze the correlation between parameters (temperature, MLR, HLR, HRT and water depth) and nutrient removal performance in different seasons through temperature-dependent first-order kinetics model (κ-C*) (Kadlec and Knight, 1996), Spearman’s rank correlation and redundancy analysis (RDA); (3) optimize the operational parameters in field-scale SMCWs for nutrient removal in plateau suburban watersheds management.
Section snippets
Site description
As an important source of drinking water for Dali city (Yunnan province, China), the water body of plateau Erhai Lake had become mesotrophic in recent years mainly because of NPS pollution. The mesotrophic Erhai Lake was now threatening local ecological system and drinking water security, which attracted attentions on the treatment of water pollution. 129 novel SMCWs had been designed and built to deal with NPS pollution in Erhai Lake basin till May 2017. Among these 129 wastewater treatment
Removal efficiency comparison
The average NH4+-N, NO3−-N, SRP, TN and TP concentrations of influent and effluent as well as REs of 63 SMCWs in dry, normal and wet season were summarized in Table 1. The average TN and TP concentrations of influent water in wet season (6.37 mg/L and 0.48 mg/L) were lower than those in normal season (8.04 mg/L and 0.59 mg/L) and dry season (8.94 mg/L and 0.54 mg/L). The higher concentration of NO3−-N than that of NH4+-N indicated that agriculture runoff was the major source of nitrate
Conclusion
Contaminants from non-point source pollution in Erhai lake basin were effectively intercepted by SMCW systems. Among the operational parameters, temperature, MLR and HLR were the most crucial factors that affected the treatment performances in different seasons. Analysis results showed that optimized parameters were helpful for the seasonal operation of SMCW systems. The relationship between seasonal variation of parameters and purification performances showed that optimized HLR and MLR should
Acknowledgements
This study was supported by the National Water Pollution Control and Treatment Science and Technology Major Project of China (grant numbers 2017ZX07401004, 2017ZX07401003). The authors sincerely thank Haibo Xie, Enrui Wang, Bei Du, Qiu Yu for assistance in sampling and analysis.
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