Elsevier

Water Research

Volume 187, 15 December 2020, 116443
Water Research

An integrated approach for enhancing the overall performance of constructed wetlands in urban areas

https://doi.org/10.1016/j.watres.2020.116443Get rights and content

Highlights

  • Develop a platform to enhance the overall performance of constructed wetlands (CWs).

  • In addition to the purification, the ecological and storage functions of CWs were considered.

  • Integration of different engineering and mathematical tools for solution optimization.

  • The optimal solution and appropriate thresholds were obtained to manage CWs.

Abstract

Constructed wetlands (CWs) are an important component of the urban matrix and play an essential role in the restoration of urban ecological environments. Although existing studies have mainly focused on the efficiency of technologies for removing pollutants in wastewater, efforts to intensify the overall performance of CWs have not been reported. Here, we propose a novel theoretical scheme for promoting optimal overall performance of CWs through the development of an integrated approach, entailing simulation, evaluation, and optimization strategies for their management. We successfully simulated the water distribution system of the Yanfangdian CW in Beijing, China, applying 42 hydrological parameters within the MIKE 21 software. We further evaluated our simulation results by performing an analytic hierarchy process to calculate performance scores. The back propagation neural network was well trained to quantify the relationship between the hydrological parameters and the overall performance of CW based on its water distribution characteristics and their corresponding scores. Subsequently, a genetic algorithm was applied to determine the hydrological solution. A strategy for optimizing the water level and flow was formulated for improving the ecological, purification and storage performances of the targeted CW along with a flexible strategy for ensuring its proper functioning. Our approach provides a robust and universal platform that can contribute significantly to the advancement of CWs that have a wide range of applications and could be extended to other ecosystems.

Introduction

In recent decades, accelerated urbanization has severely impacted on the health and ecological functions of urban environments (Peng et al., 2017; Suriya and Mudgal, 2012), which is reflected in water pollution, destruction of vegetation, and reduced species diversity (Luo et al., 2018; Schueller et al., 2019; Yuan et al., 2018). Moreover, the degradation of ecological environments undermines the progress of social and economic development (United Nations, 2018). Therefore, in a context of increasing urbanization, the immediate challenge lies in ensuring the sustainability of social and economic development, which entails advancing economic growth while maintaining environmental quality. (Luukkanen et al., 2019; Schneider et al., 2010; Shen et al., 2016).

Constructed wetlands (CWs), which are widely acknowledged to be a feasible and effective approach for improving the environmental quality of cities, can play an important role in restoring ecological environments (Maucieri et al., 2017; Saeed and Khan, 2019; Vymazal and Březinová, 2016; Wu et al., 2018; Zhang et al., 2014). CWs are artificial functional systems comprising wetland vegetation, beds, and shallow ponds or trenches that can be engineered for multiple purposes, such as wastewater treatment and wildlife habitat restoration (Drayer and Richter, 2016; Matamoros et al., 2008; Vymazal, 2014; Zhang et al., 2020). They are generally classified as surface flow or subsurface flow wetlands. A particular advantage of CWs is that their design can be specifically tailored to address locally specific environmental issues, thus providing strong support for sustainable local development (Mitsch et al., 2015; Saeed et al., 2018; Sharley et al., 2017). Considerable efforts have been made to enhance the effects of CWs in improving environment quality through the optimization of design schemes and the development of management strategies (Kaseva, 2004; Nesbit and Mitsch, 2018; Saeed et al., 2020; Saeed and Sun, 2017; Vymazal, 2017; Wang et al., 2018; Yakar et al., 2018; Zhao et al., 2020). For example, the subsurface flow zone of CWs is usually designed for the purpose of reducing pollutants using technologies such as intermittent aeration, batch operations, tidal flows, front aeration and internal recirculation that have proven effective in removing pollutants from wastewater (Avila et al., 2013; Chen et al., 2017; Fan et al., 2013; J. Li et al., 2019; Lin et al., 2020). Moreover, the inclusion of biochar, silver nanoparticles, and microalgal biomass can enhance the efficiency of treatments of subsurface flow systems to remove pollutants (Huang et al., 2017; Kasak et al., 2018; X. Liu et al., 2018; Saeed et al., 2019b; Song et al., 2016; Zhong et al., 2019).

The functions of the surface flow zone of CWs, which also undergoes treatment to remove pollutants, extend further to include the restoration of ecologies and landscapes as well as water regulation and storage, which are directly related to the overall performance of CWs (Matamoros et al., 2008; Perkins and Hunter, 2000). However, because of its exposure to outside elements, the surface flow zone is susceptible to a variety of external factors, such as species invasion and climate change. Consequently, a hot research topic focuses on the question of how to achieve overall performance intensification of CWs through the functional optimization of the surface flow zone (Abbasi et al., 2018; Harrington and Scholz, 2010; Lavrnić et al., 2020; Lopardo et al., 2019; Saeed et al., 2019a). The effectiveness of the performance of the surface flow zone is mainly determined by the hydraulic residence time (HRT), pollution load, water depth, aquatic plants, and water storage capacity. The HRT is a key parameter that varies with the age of wetlands (Lavrnić et al., 2020), and affects the efficiency of the process of removing pollutants from wetland systems (Díaz et al., 2010; Merlin et al., 2002). Tracing technology and computational simulations are widely used to determine the HRT (Harrington, 2019; Kusin, 2013; Zahraeifard and Deng, 2011). The findings of research focusing on the relationship between HRTs and the efficiency of pollutant treatments have prompted the use of CWs in the treatment of agricultural runoff and industrial and domestic sewage (Díaz et al., 2012, 2009; Guo et al., 2019; Huang et al., 2000; Pugliese et al., 2020; Ran et al., 2004). In addition to HRT, water depth and aquatic plant coverage, which are linked to biochemical reactions and wildlife activity (Aguirre et al., 2005; García et al., 2005; Zuo et al., 2004), are important factors influencing the design of the surface flow zone. Variations in the water depth evidently determine not only available food resources that make these water bodies attractive habitat for wildlife, but also the composition and abundance of aquatic plants that grow in wetlands. In turn, these aquatic plants, alter the environmental conditions, habitats, and quantity of available food for wildlife (Baschuk et al., 2012; Gorgoglione and Torretta, 2018). Extensive investigations performed to determine appropriate water depths and selections have contributed significantly to engineering designs for optimizing wetland performance (Aguirre et al., 2005; Drayer and Richter, 2016; Guo et al., 2017; D. Li et al., 2019; Russell, 1999; Verhofstad et al., 2017). However, despite several advances in research on the design of CWs and strategies for their management, previous studies have mainly focused on the removal of pollutants from wastewater. Consequently, the establishment of a general platform for developing strategies for managing CWs that can enhance their overall performance (ecological, purification and storage) remains a gap in the literature.

Because enhancing the overall performance of an ecosystem requires a systematic process that usually entails environmental simulation, comprehensive evaluation, and solution optimization, different engineering and mathematical tools often need to be integrated to deal with the corresponding aspects. These tools include hydrodynamics software, environmental models, a geographic information system, remote sensing, artificial neural network, the analytic hierarchy process and the genetic algorithm (Chowdary et al., 2013; Huo et al., 2013; Y. Liu et al., 2018; Paliwal and Patra, 2011; Veisi et al., 2016), some of which have been systematically integrated for the assessment and restoration of ecosystems (Li et al., 2020; Youssef et al., 2011). Our objective was to establish a universal integrated approach for achieving reasonable management strategies to enhance the overall performance of CWs (ecological, purification and storage). Fig. 1 depicts a scheme for developing such an integrated approach. The MIKE 21 modeling system was used to simulate the water distribution system, and the analytic hierarchy process (AHP) was applied to evaluate the simulation results. Ultimately, a strategy for achieving optimized hydrological control was developed by the back propagation (BP) neural network and the genetic algorithm (GA). The significance of this study lies in its establishment of a complete set of procedures for simulating, evaluating, and optimizing the ecological and non-ecological performances of CWs, thus providing valuable guidelines for the efficient functioning of wetlands and other ecosystems.

Section snippets

Description of the study area

The Beiyun River in China is one of Beijing's main drainage channels into which 76% of the city's total wastewater is discharge. Given the influx of a large volume of wastewater into this river, its ecological environment is facing severe challenges (BMEPB, 2012). The Yanfangdian CW was designed and built in the downstream beach area of the Beiyun River (at 39°45′36.80" and 116°51′45.33") to improve the river's ecological environment and water quality. This CW, which is 10.7 km long, is located

The simulation of the water system and the evaluation based on the scoring

Prior to conducting the simulation of the water distribution system of the Yanfangdian CW, we conducted verification experiments to test whether the MIKE 21 model was a good fit for the study area. The elevation distribution of the study area was obtained through the verification tests (Fig. 5.a), and provided the geographical basis. For a particular flow condition, we selected four typical sections of the Beiyun River to simulate variations in the water level using the MIKE 21 modeling system.

Conclusion and implications

Previous studies have mainly focused on engineering design and pollutant treatment processes of CWs without exploring ways of improving their overall performance. In this study, we developed an integrated approach, entailing the systematic application of simulation tools and multiple data analysis methods. This approach provides a general and robust platform for developing strategies for improving the overall performance (ecological, purification, and storage) of the Yanfangdian CW. Using this

CRediT authorship contribution statement

Renkuan Liao: Writing - original draft, Writing - review & editing, Formal analysis, Visualization. Zhengyuan Jin: Writing - original draft, Investigation, Formal analysis, Data curation, Software. Minghong Chen: Resources, Methodology, Writing - original draft. Shuqin Li: Project administration, Funding acquisition, Conceptualization, Methodology, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the National Key Research and Development Program (2016YFC0400107), National Water Pollution Control and Treatment Science and Technology Major Project (2017ZX07103-006) and China Postdoctoral International Cooperation Program (20180003).

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    Renkuan Liao and Zhengyuan Jin contributed equally to this work.

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