Sustainable Solutions for Environmental Pollution, Volume 2. Группа авторов
The intensity of the green channel (560 nm) reflects the development of emerged and free-floating vegetation, as shown in Figure 1.9. It is a global assessment as the CW is composed of different zones (e.g., phytoplankton, reedbeds, or free-surface area) (pole-zhi.org, 2013). The vegetation starts to grow at the end of March and reaches its maximum (as monitored though the green channel) early June.
Figure 1.8 Sentinel-2 True colour images of the St Just–St Nazaire de Pézan CW in winter and summer 2020. The dash area corresponds to the CW.
Figure 1.9 Monitoring of vegetation on the St Just–St Nazaire de Pézan CW using Sentinel-2 satellite images.
In case of larger wetlands, more detailed analysis of the vegetation development can be done. The Tres Rios system, downstream a wastewater treatment plant in Phoenix (Arizona), is composed of several CWs (280-ha total area) (Figure 1.10). The western CW has an area of 42 ha. The vegetation is mainly composed of marshes along the shores. The open water depth varies between 1.5 and 2 m (Bois et al., 2017). The normalized difference vegetation index (NDVI) is one of the many descriptors available to track the live green vegetation in the marshed:
Figure 1.10 Sentinel-2 satellite view of the Tres Rios (Phoenix, Arizona) FSF-CW on September 4, 2020. The dashed area corresponds to the monitored fringing marshes.
Figure 1.11 Monitoring of Tres Rios fringing marshes using satellite images.
where Red and NIR stand for the spectral reflectance measurements acquired in the red (665 nm) and near-infrared (842 nm) regions, respectively. As the sky in Phoenix is often cloudless, the data rate is much higher than in the French example. According to the NDVI profile, the green vegetation starts to grow in February and the maximum occurs in September (Figure 1.11). This is a little later than the time of the maximal aboveground plant biomass (July), as recorded between 2012 and 2015 (Bois et al., 2017).
1.14 Wetland Modeling
There are two levels of modeling: modeling of specific processes, such as plant development or pollutant fate, and general modeling, taking into account all (or most of all) processes taking place in a wetland. Modeling have several purposes such as: 1) help in understanding a complex set of entangled processes, 2) definition of a control strategy, although the handles are usually limited to flow control, or 3) forecasting the development of a wetland to define the best maintenance strategy on the long term such as vegetation harvesting or sediment removal.
1.14.1 Aquatic Plant Development Models
Due to their large distribution, both in natural wetlands and CWs, Phragmites sp. have received a lot of interest in terms of modeling their seasonal life cycle. The model proposed by Asaeda and Karunaratne (2000) and Asaeda et al. (2002) takes into account the plant roots, rhizomes, shoots, stems, leaves, and panicles, whose development is governed by air temperature and solar irradiation (Asaeda and Karunaratne, 2000; Asaeda et al., 2002). After senescence, leaching, mineralization, and nutrient fixation of the litter are also modeled. This model has served as a basis for the modeling of the seasonal life cycle of Typha sp. (Eid et al., 2012).
1.14.1.1 Submerged Aquatic Plants
In the early 1980s, Best (1981) proposed a mechanistic model to describe the seasonal growth and primary production of C. demersum. Its growth is governed by light and temperature (Best, 1981). Light availability at a certain water depth is obeying to the Beer-Lambert’s law and is also a function of the biomass present above (shading effect). The model incorporates the effect of photosynthesis and the senescence. Herb and Stefan have added the daily variation of solar irradiance (Herb and Stefan, 2003) and studied the effect of the competition between two submerged species (Herb and Stefan, 2006). A general additive model has been proposed by Yang et al. (2020): the global biomass growth was explained by four individual variables (water depth, transparency, Ntot, and Ptot) and two combined variables (water depth × transparency and water depth × Ptot). The model was tested on a biomass composed of 10 species, Potamogeton crispus and Ceratophyllum demersum being the most common species found in twelve sites of a shallow lake (Baiyangdian Lake, Hebei province, China) (Yang et al., 2020).
1.14.1.2 Duckweed
Duckweed is a widespread floating plant family, whose five main genera are Lemna, Spirodela, Wolffia, Wolffiella, and Landoltia (Zhang et al., 2014b). Their growth, based on Monod’s equations, can be described in function of temperature, light, and nutrients (N and P). The mechanistic model developed by Peeters et al. (2013) takes also into account losses due to mortality, grazing, and respiration (Peeters et al., 2013). Growth is limited by crowding, meaning that when there is no more enough space (around 180 g dry weight per square meter) duckweed stops to develop (Driever et al., 2005). Although a general modeling structure can be proposed, parameters may differ according to the species, some of them being for example favored by high nutrient availability (Njambuya et al., 2011). In the case of mixed vegetation, duckweed mats induce shading for submerged plants and algae, i.e., a decrease of the solar irradiance necessary for their photosynthesis. In large surface-flow CWs wind can displace duckweed mats, changing their thickness and therefore the shading of submerged vegetation.
1.14.2 Micropollutants Sorption
To study the kinetics of sorption, on plants or sediments, of any micro-pollutant (metal or organic micropollutant), two factors should be considered: 1) how much micropollutant is attracted by the sorbent; 2) to which extent this micropollutant is retained on sorbent in an immobilized form. In order