SURFWET: A biokinetic model for surface flow constructed wetlands
Graphical abstract
Introduction
Constructed wetlands (CWs) are an alternative biotechnology for wastewater treatment. In these systems, pollutant removal is done through complex physical, chemical and biological processes (Kadlec and Wallace, 2009), where different types of microorganisms intervene. Although the effectiveness of these treatment systems has been proven by numerous experiences, studying the influence of a great variety of variables over their internal functioning is still an active topic of research (Sanchez-Ramos et al., 2017; Gorgoglione and Torretta, 2018).
Since the mid-1980s, different mathematical models for CWs have been developed (Kadlec and Wallace, 2009). Initial models were purely experimental, input-output models (black-box), not focusing much on internal processes (Mitsch and Gosselink, 1986; Kadlec and Knight, 1996). In more recent times, first process-based models have appeared (Meyer et al., 2015). Mathematical models of CWs can be useful for several purposes including: i) understanding of internal processes; ii) predictions under different environmental and management scenarios; and iii) design optimization (Meyer et al., 2015; Anderssen et al., 1996; Jin and Ji, 2015). In practice, designing CWs is still based on empirical methods.
Current state of the art on CWs modeling consists of process-based approaches, including new mathematical descriptions and also new numerical simulations trying to validate them (Meyer et al., 2015). Most models have been developed for subsurface flow CWs (Langergraber et al., 2009; Mburu et al., 2012; Samso and Garcia, 2013), whereas the state of the art of surface flow CWs is not very extensive (Carleton and Montas, 2010; Kumar and Zhao, 2011; Aboukila and Deng, 2018).
Most CWs models have been focused on specific processes, but the current trend is to increase the number of processes considered and, at the same time, to numerically simulate long-term scenarios (Samso et al., 2015). In parallel, other recent works present design-support models, simpler and more practical for designing purposes. Both types of approaches are useful: the first ones from a scientific viewpoint and the second ones from an engineering viewpoint. A common problem which affects all of them is the lack of experimental data for validation purposes (Meyer et al., 2015).
Furthermore, some models allow to analyze the effect of design parameters on treatment efficiency of CWs (Sanchez-Ramos et al., 2017; Cancelli et al., 2019). All in all, current developments have a broad spectrum of aims, are made for distinct types of wetlands and require different computational resources (Meyer et al., 2015). Mathematically, a wide range of approaches can be found, from statistical regressions to partial differential equations (Brito-Espino et al., 2020).
Specifically, surface flow CWs models try to incorporate processes such as removal of cadmium (Pimpan and Jindal, 2009), organic matter (Ophithakorn et al., 2013), phosphorus (Marois and Mitsch, 2016), nitrogen (Gargallo et al., 2017a), the role of vegetation (Galanopoulos et al., 2013), sedimentation and resuspension of dispersed solids (Gargallo et al., 2017b). There are few cases of surface flow CWs models with a more holistic approach, which attempt to represent the whole ecosystem rather than focusing on very specific processes (Sanchez-Ramos et al., 2019). As an example, the effects of rainfall and evapotranspiration can be highly important in surface flow CWs. However, this type of physical processes are not usually included in existing models.
In summary, the understanding of the internal functioning of CWs is still in an early stage of development (Sanchez-Ramos et al., 2019), which limits their spreading. To achieve a more realistic modeling it is necessary to increase the number of processes included, seeking a balance between the level of complexity of the models and their usability (Jorgensen and Fath, 2011).
The purpose of this work is to present a general process-based model for surface flow CWs (SURFWET), including the main water quality parameters in wastewater treatment systems (e.g., dissolved oxygen, organic matter, nitrogen, phosphorus and suspended solids), as well as the main actors involved in biochemical processes (e.g., bacteria, macrophytes and phytoplankton) and physical processes (wind, avifauna, infiltration, rainfall and evapotranspiration).
Since the state of the art of surface flow CWs modeling is not very extensive yet, a mathematical model with a holistic approach like SURFWET can help to improve the understanding of their internal processes, which can be useful to enhance their operation and performance. Even if surface flow CWs often have lower removal efficiencies than subsurface flow CWs, they also provide significant ancillary benefits, primarily in the form of human uses and wildlife habitats (Kadlec and Wallace, 2009). In addition, features of surface flow CWs make them a biotechnology suitable for wastewater post-treatment (polishing), stormwater treatment (because of their ability to deal with pulse flows and changing water levels), and mine waters treatment, to name a few.
The present model has been applied to simulate the functioning of a surface flow CW proposed for improving treatment of sewage effluents which could affect a Mediterranean natural reserve of international importance. Due to the lack of experimental data (the CW has not been built), the model could not be calibrated and validated, but experimental inputs (e.g., water quality of the sewage effluents, meteorological data) have been used in the simulation. Nevertheless, the main objective is to provide a general formulation for surface flow CWs useful to be implemented in different simulation tools and to address novel and/or increasingly-complex scenarios.
Section snippets
Materials and methods
In order to develop this study, we first carried out a review of the scientific literature, focusing on mathematical modeling of CWs. Our goal was to develop a comprehensive process-based model, and to include the main water quality parameters with a holistic approach. In the formulation, only reaction equations were considered, and not spatially-dependent processes such as advection and diffusion.
Then, the model was formulated in terms of a system of ordinary differential equations, coupling
Results and discussion
SURFWET has been numerically tested to simulate long-term scenarios, corresponding to periods considerably longer than usual. In this section, we report a 12-year simulation of functioning of a surface flow CW proposed to improve water quality of the TSE from Daimiel municipality, Castilla-La Mancha, Spain.
Conclusions
The implementation of the SURFWET mathematical model and the simulation of a specific case study has shown the importance of dissolved oxygen for an accurate modeling, because of its influence on many processes and variables. As expected for this biotechnology, most water quality variables display a seasonal behavior in the wetland, clearly conditioned by biological cycles of vegetation, which have been incorporated, such as growth and degradation seasonality. The model has also been able to
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.
Acknowledgments
The authors wish to thank Luis Rodríguez for his help with stoichiometric coefficients, Máximo Florín for his help in understanding the role of vegetation in photosynthesis, Álvaro Galán for his help in clarifying the concept and formulation of wave excursion amplitude, and Rohit Namjoshi for additional help regarding graph styling. The authors also wish to thank the anonymous reviewers who provided useful comments that helped to improve the manuscript.
This research did not receive any specific
References (62)
- et al.
Two variable residence time-based models for removal of total phosphorus and ammonium in free-water surface wetlands
Ecol. Eng.
(2018) - et al.
Seasonal decomposition of Typha latifolia in a free-water surface constructed wetland
Ecol. Eng.
(November 2006) - et al.
Decision support for the design of constructed wetlands
Appl. Math. Model.
(1996) - et al.
Dynamic modeling of the growth of Phragmites australis: model description
Aquat. Bot.
(2000) - et al.
Modelling the effects of macrophytes on algal blooming in eutrophic shallow lakes
Ecol. Model.
(1997) - et al.
Application of a mathematical model to predict simultaneous reactions in anaerobic plug-flow reactors as a primary treatment for constructed wetlands
Sci. Total Environ.
(January 2020) - et al.
Development and evaluation of a mechanistic model to assess the fate and removal efficiency of hydrophobic organic contaminants in horizontal subsurface flow treatment wetlands
Water Res.
(2019) - et al.
An analysis of performance models for free water surface wetlands
Water Res.
(2010) - et al.
Effect of wastewater management on phosphorus content and sedimentary fractionation in Mediterranean saline lakes
Sci. Total Environ.
(2019) - et al.
Sediment resuspension by wind in a shallow lake of Esteros del Iberá (Argentina): a model based on turbidimetry
Ecol. Model.
(2005)
A pilot-scale study for modeling a free water surface constructed wetlands wastewater treatment system
Journal of Environmental Chemical Engineering
Biokinetic model for nitrogen removal in free water surface constructed wetlands
Sci. Total Environ.
Sedimentation and resuspension modelling in free water surface constructed wetlands
Ecol. Eng.
Which are the most sensitive parameters for suspended solids modelling in free water surface constructed wetlands?
Environ. Model. Softw.
Simple equations to represent the volume-area-depth relations of shallow wetlands in small topographic depressions
J. Hydrol.
Modelling nitrogen and phosphorus cycling and retention in Cyperus papyrus dominated natural wetlands
Environ. Model. Softw.
An integrated environment model for a constructed wetland – hydrodynamics and transport processes
Ecol. Eng.
A review on numerous modeling approaches for effective, economical and ecological treatment wetlands
J. Environ. Manag.
Modeling phosphorus retention at low concentrations in Florida Everglades mesocosms
Ecol. Model.
Simulation of carbon, nitrogen and sulphur conversion in batch-operated experimental wetland mesocosms
Ecol. Eng.
Modelling constructed wetlands: scopes and aims - a comparative review
Ecol. Eng.
An evaluation of the application of treated sewage effluents in Las Tablas de Daimiel National Park, Central Spain
J. Hydrol.
Simulation modelling of dissolved organic matter removal in a free water surface constructed wetland
Ecol. Model.
Mathematical modeling of cadmium removal in free water surface constructed wetlands
J. Hazard. Mater.
BIO-PORE, a mathematical model to simulate biofilm growth and water quality improvement in porous media: application and calibration for constructed wetlands
Ecol. Eng.
Effect of key design parameters on bacteria community and effluent pollutant concentrations in constructed wetlands using mathematical models
Sci. Total Environ.
Effects of flooding regime and meteorological variability on the removal efficiency of treatment wetlands under a Mediterranean climate
Sci. Total Environ.
Development and evaluation of a process-based model to assess nutrient removal in floating treatment wetlands
Sci. Total Environ.
Development of a constructed subsurface-flow wetland simulation model
Ecol. Eng.
Engineering Fluid Mechanics
WASP4, a hydrodynamic and water quality model: Model theory
Cited by (5)
Experimental checking and modeling of the influence of operation conditions on the first order kinetic constants in free water surface wetlands
2023, Journal of Environmental ManagementEffect and Process of Ecological Pond-Submerged Surface Flow Composite Constructed Wetland on Water Quality Purification of Rivers Entering Fuxian Lake
2024, Research of Environmental SciencesComparison of simple models for total nitrogen removal from agricultural runoff in FWS wetlands
2022, Water Science and TechnologySimultaneous Use of Mass Transfer and Thermodynamics Equations to Estimate the Amount of Removed Greenhouse Gas from the Environment by a Stream of Water
2021, Environmental Modeling and Assessment