SURFWET: A biokinetic model for surface flow constructed wetlands

Highlights

• A biokinetic model for surface flow constructed wetlands is presented.

• Key agents on pollutant removal are considered (N, P, COD and TSS).

• The hydraulic model considers water volume as variable with water inputs and outputs.

• DO was considered a key driver of the model, influencing particularly N and COD.

• The model can be used for optimizing empirical designs of constructed wetlands.

Abstract

Constructed wetlands are an alternative biotechnology for wastewater treatment that have several advantages over conventional systems. In this work, a biokinetic model for surface flow constructed wetlands is presented (SURFWET). SURFWET belongs to a class of models that are not only interesting from a theoretical viewpoint, as they allow to improve the understanding of the underlying processes; but also from a practical viewpoint, because they can be useful for optimal designs of constructed wetlands, complementing current empirical methods. The proposed model is centered on the intervening physical and biochemical processes involved in pollutant removal in wastewater (organic matter, nitrogen, phosphorus, suspended solids), capturing the interplay of the main agents on contaminant removal (bacteria, macrophytes and phytoplankton). Furthermore, the hydraulic model considers water volume as a variable depending on the outlet hydraulic capacity, and dissolved oxygen has also been introduced as a key driver of reaction kinetics of wetlands. Beyond putting forward a theoretical framework, SURFWET has been applied to simulate a specific case to demonstrate its robustness, in a 12-year-interval simulation. The results show the typical seasonality of this biotechnology, highlighting the importance of dissolved oxygen, which is a key limiting factor on a large number of biochemical processes.

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.