Next Article in Journal
Natural and Historical Heritage of the Lisbon Botanical Gardens: An Integrative Approach with Tree Collections
Next Article in Special Issue
Influence of the Nitrate-N to Ammonium-N Ratio on Relative Growth Rate and Crude Protein Content in the Duckweeds Lemna minor and Wolffiella hyalina
Previous Article in Journal
Chitosan-Induced Activation of the Antioxidant Defense System Counteracts the Adverse Effects of Salinity in Durum Wheat
Previous Article in Special Issue
Brassinolide Enhances the Level of Brassinosteroids, Protein, Pigments, and Monosaccharides in Wolffia arrhiza Treated with Brassinazole
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 10
Issue 7
10.3390/plants10071366
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Density Dependence Influences the Efficacy of Wastewater Remediation by Lemna minor

by
Éamonn Walsh
1,2,*,
Neil E. Coughlan
1,2,
Seán O’Brien
1,2,
Marcel A. K. Jansen
1,2 and
Holger Kuehnhold
3
1
School of Biological, Earth and Environmental Science, University College Cork, Distillery Fields, North Mall, T23N73K Cork, Ireland
2
Environmental Research Institute, University College Cork, Lee Road, T23XE10 Cork, Ireland
3
Department of Ecology, Leibniz Centre for Tropical Marine Research (ZMT), 28359 Bremen, Germany
*
Author to whom correspondence should be addressed.
Plants 2021, 10(7), 1366; https://doi.org/10.3390/plants10071366
Submission received: 25 May 2021 / Revised: 28 June 2021 / Accepted: 30 June 2021 / Published: 3 July 2021
(This article belongs to the Special Issue Duckweed: Research Meets Applications)
Browse Figures
Versions Notes

Abstract

:
As part of a circular economy (CE) approach to food production systems, Lemnaceae, i.e., duckweed species, can be used to remediate wastewater due to rapid nutrient assimilation and tolerance of non-optimal growing conditions. Further, given rapid growth rates and high protein content, duckweed species are a valuable biomass. An important consideration for duckweed-mediated remediation is the density at which the plants grow on the surface of the wastewater, i.e., how much of the surface of the medium they cover. Higher duckweed density is known to have a negative effect on duckweed growth, which has implications for the development of duckweed-based remediation systems. In the present study, the effects of density (10–80% plant surface coverage) on Lemna minor growth, chlorophyll fluorescence and nutrient remediation of synthetic dairy processing wastewater were assessed in stationary (100 mL) and re-circulating non-axenic (11.7 L) remediation systems. Overall, L. minor growth, and TN and TP removal rates decreased as density increased. However, in the stationary system, absolute TN and TP removal were greater at higher densities (50–80% coverage). The exact cause of density related growth reduction in duckweed is unclear, especially at densities well below 100% surface coverage. A further experiment comparing duckweed grown at ‘low’ and ‘high’ density conditions with the same biomass and media volume conditions, showed that photosynthetic yield, Y(II), is reduced at high density despite the same nutrient availability at both densities, and arguably similar shading. The results demonstrate a negative effect of high density on duckweed growth and nutrient uptake, and point towards signals from neighbouring duckweed colonies as the possible cause.

1. Introduction

Globally, the provision of nutritious food is a challenging endeavour [1,2]. Climate change, a reduction in the per capita availability of arable land, as well as soil erosion, chemical overuse and finite resources have decreased food security [3,4,5,6]. In recent years, the adoption of circular economy (CE) principles in food production systems has been suggested as a mechanism to improve resource-efficiency and the sustainability of food production [7]. In essence, CE promotes long-term retention and reuse of resources, as well as minimisation of waste generation, resulting in a reduced need for raw materials [8]. Thus, CE principles encourage the adoption of closed-loop production patterns, whereby waste is appropriated as a resource [8], reducing emissions and energy consumption in the process [9].
Dairy products are a major and important source of nutrition, employment and trade worldwide [10,11]. However, large volumes of wastewater are created as a consequence of dairy production and processing. It is estimated that up to 10 L of wastewater is created per litre of milk processed, making dairy processing waste one of the most significant waste streams in the food industry [12]. Dairy processing wastewaters tend to contain particularly high concentrations of organic matter, measured as chemical oxygen demand (COD): 2000–6000 mg L−1 COD [13]; 4420 mg L−1 COD [14]; 55,430–70,150 mg L−1 COD [15]. Moreover, these wastewaters generally contain high concentrations of nutrients, especially ammonium (64–270 mg L−1 NH4-N), nitrate (9–30 mg L−1 NO3-N) and phosphate (20–356 mg L−1 PO4-P) [13,15,16]. The disposal of such wastewater often lacks value-capture in the treatment process [17]. For example, valuable nitrogen-containing nutrients such as nitrate and ammonium are commonly released as gaseous N2 [12]. Phosphate is typically precipitated using aluminium chloride, lime and similar additives, to generate a precipitate sludge [18]. The resulting non-soluble form of phosphate has arguably limited further benefit as a fertiliser [19].
Phytoremediation has been proposed as a viable alternative to traditional wastewater treatments, as phytoremediation removes plant nutrients from wastewaters and also retains these elements in a chemical form suitable for further use [20,21]. Dairy processing wastewater is considered to be a good candidate for phytoremediation as it generally contains an abundance of essential plant nutrients, such as ammonium, nitrate and phosphate [22].
Duckweed, Lemnaceae, are a family of floating aquatic plants with excellent potential for phytoremediation due to a tolerance of wastewater conditions [23,24,25], fast growth rates [26] and high protein or starch content [27,28], as well as demonstrated use as feed, food and biofuel [29,30,31]. Thus, these plants can combine efficient wastewater remediation with the creation of a valuable plant biomass. To date, few studies have attempted to assess the suitability of duckweed for remediation of dairy processing wastewater. However, in principle duckweed has been shown to remediate dairy processing wastewater that lacks organic components, such as sugars and fats [32]. As a high proportion of these organic components are generally removed by existing microbial-based treatment technologies, such as sequential batch reactors or anaerobic digesters [33,34], the incorporation of duckweed into the remediation process is a realistic approach.
Wastewater remediation by duckweed is a surface process, whereby a layer of duckweed takes up nutrients from the underlying water column. In their natural habitats, most duckweed species grow in dense, floating mats [35]. Once mats have filled the available space, individual colonies begin to overlap and shade each other. Such highly crowded conditions negatively impact duckweed growth rates [36,37], and duckweed may even start to senesce and release nutrients back into the water column [38]. Conversely, a higher duckweed plant density can increase the potential for uptake of nitrogen and phosphorus [39]. Given the implications for biomass production and wastewater remediation [40], an improved understanding of the relationship between plant surface density and biomass yield, as well as net nutrient uptake is required. Earlier work has shown that a low growth rate at a high plant density does not necessarily imply a low biomass yield or low N and P removal [37]. Accordingly, to achieve effective phytoremediation, determination of optimal duckweed density for nutrient removal, plant growth and biomass yield per water surface area is required. In the present study, the effects of density on duckweed growth and remediation were quantified. This was done under axenic conditions, using stationary tanks containing either synthetic dairy processing wastewater or an optimal medium (half-strength Hutner’s). Furthermore, with the aim of reproducing some of the conditions of large-scale duckweed phytoremediation systems, L. minor was cultivated on synthetic dairy wastewater using a larger scale non-axenic re-circulatory system. The results will inform management of duckweed-based remediation systems.

2. Materials and Methods

2.1. Stock Cultivation

The duckweed strain used in this study was Lemna minor L.–Blarney, strain number 5500 in the Rutgers Duckweed Stock Cooperative database [41]. A sterile stock of L. minor was cultivated on half-strength Hutner’s medium [42] under an average light intensity of 50 µmol m−2 s−1 photosynthetically active radiation (PAR) in a controlled growth-room (22 °C, 14 h:10 h light:dark photoperiod).

2.2. Experimental Design

2.2.1. Synthetic Dairy Processing Wastewater

The synthetic dairy processing wastewater used in this study is based on the composition of real dairy processing wastewater found in dairy wastewater treatment facilities [43], with modifications as detailed in Walsh et al. [21]. The pH was reduced to, and maintained at, around 5.0 from a natural value of 8 with 1 M H2SO4 to facilitate optimal L. minor growth [44]. H2SO4 was chosen to decrease pH as SO4-S has a wide ‘optimal’ range (0.5–20 mM) in which it does not cause adverse or beneficial effects towards duckweed, with a high maximum tolerated concentration of 60 mM, as per Walsh et al. [21].

2.2.2. Manipulation of Plant Density

In this paper, the term “plant density” is used to refer to the relative surface cover of the medium by Lemna minor, i.e., the proportional cover by duckweed as a fraction of the total available surface area. Plant density, i.e., surface cover, is linked to plant biomass per m2. Plant biomass always refers to fresh duckweed biomass. Plant density was either measured directly, or estimated based on biomass per surface area. Direct density measurements were performed using the imaging software Easy Leaf Area [45] which distinguishes duckweed frond surface cover from non-duckweed covered surface area. This non-invasive technique could be used throughout the duration of an experiment. Alternatively, plant density, i.e., relative surface cover, was estimated based on biomass per m2 of surface area. In this scenario, the latter values were calculated using a calibration curve for L. minor biomass versus surface area. To generate a calibration curve, a number of colonies were taken at random from a stock culture acclimated to the relevant medium. The total surface area and mass of these ‘representative’ colonies were measured and the area/mass ratio was calculated.

2.2.3. Stationary Remediation Experiment 1: Growth and Remediation at Variable Plant Densities

Two stationary experiments were conducted. In the first, scoping, experiment L. minor was grown on 100 mL of synthetic dairy wastewater for seven days (days 0–7) using a range of eight density conditions (10, 20, 30, 40, 50, 60, 70, 80% plant coverage of total surface area, n = 6; Figure 1). The corresponding biomass per container surface area (ranging from 21 to 154 g m−2) was estimated based on a mass/area ratio of L. minor biomass. Plants were kept in Magenta vessels (GA-7, surface area (SA) 42.24 cm2) in a controlled growth room (average light intensity 50 µmol m−2 s−1 PAR, 22 °C, 16 h:8 h light:dark photoperiod). To start experiments, L. minor colonies were taken at random from stock cultures that had been acclimated to synthetic wastewater for seven days. The range of density conditions was created by adding varying numbers of L. minor colonies and determining the total frond surface cover using Easy Leaf Area imaging. Plant densities were maintained at ±2% of target surface cover throughout the experiment by removing excess plant material every 2–3 days, and this process was guided by measurements of frond surface area, as determined by Easy Leaf Area. Excess plant biomass removed throughout the experiment was weighed and used to calculate specific growth rate (SGR) and relative growth rate (RGR) (n = 6, except for 40% where n = 4). Total nitrogen (TN) and total phosphorous (TP) were measured from medium samples taken on days 0 and 7 (n = 6, except for 40% where n = 4). Protein content was measured from plant samples taken on day 7 (n = 6, except for 40% where n = 4).

2.2.4. Stationary Remediation Experiment 2: Growth and Remediation at Low and High Density

In the second stationary experiment, the negative effect of higher duckweed density on growth was explored in greater detail. To achieve this, two density conditions were created by using two containers with different surface areas, but containing the same medium volume (200 mL) and initial plant biomass (2 g; Figure 1). For both density conditions, L. minor was grown for seven days (days 0–7) on either synthetic wastewater or half-strength Hutner’s medium. The experiment was conducted in a controlled growth room (average light intensity 50 µmol m−2 s−1 PAR, 22 °C, 16 h:8 h light:dark photoperiod). The plant density conditions consisted of low (25%; 193 g m−2) and high (60%; 476 g m−2) plant coverage of the total surface area (n = 4 per experimental treatment). The % density was determined using Easy Leaf Area imaging, while the density in g m−2 was calculated based on the ratio of the weighted inoculum (2 g) and the container surface area. The two densities were created by using two types of growing containers (Magenta vessels with 42.25 cm2 SA for 60% cover and larger circular glass containers with 103.87 cm2 SA for 25% cover). In both cases, 2 g of colonies were selected randomly from stock cultures, which had already been acclimated to their respective media for seven days within the controlled growth room. Densities were maintained at ±2% of target surface cover throughout the experiment through the removal of excess plant material every 2–3 days, a process guided by measurements of frond surface area, as determined by Easy Leaf Area. Excess plant biomass removed throughout the experiment from each replicate was weighed and used to calculate SGR and RGR (n = 4). Chlorophyll a fluorescence measurements were taken on randomly selected plants on days 0 and 7 (n = 4). TN and TP were measured from medium samples taken on days 0 and 7 (n = 4). In both stationary experiments, any water loss due to evaporation was countered by adding deionised water to maintain original volumes.

2.2.5. Re-Circulating Remediation System: Growth and Remediation at Variable Plant Densities

To determine the effect of plant density on duckweed growth and remediation capacity under more realistic operating conditions, L. minor was grown in a non-axenic, re-circulating system containing 11.7 L of synthetic dairy wastewater, for five days (days 0–5), and at three densities (20, 50 and 80% plant coverage of total surface area of 600 cm2, n = 4). Plant density was measured using Easy Leaf Area, whilst the corresponding biomass per container surface area (ranging from 50 to 187 g m−2) was estimated based on a mass/area ratio of L. minor biomass. The experiment was conducted within a controlled environment room (300 µmol m−2 s−1 PAR, 16 h:8 h light:dark photoperiod). In this experimental system, synthetic wastewater was re-circulated between two tanks, an upper duckweed tank and a lower sump tank at a rate of 125 L per hour. The upper tanks of each replicate treatment were seeded at their respective plant surface densities on the initial day of the experiment, using stock plants acclimated to synthetic wastewater for seven days. Excess biomass grown over the course of the experiment was removed twice over the five-day experiment to maintain densities within ±2% of target surface coverage, as determined using Easy Leaf Area imaging. Excess plant biomass removed throughout the experiment from each replicate was weighed and used to calculate SGR (n = 4). TN and TP were measured from medium samples taken on days 0 and 5 (n = 4). Protein content was measured from plant samples taken on day 5 (n = 4).

2.3. Measured Parameters

2.3.1. Growth

All plant biomass was dried with absorbent tissue-paper to remove excess water and ensure reliable measurements before weight measurements. A specific growth rate (SGR), for growth comparisons within the present study, was calculated from estimations and measurements of fresh biomass using the formula [46]:
S G R = W 2 / W 1 Δ T
where W1 is starting mass, W2 is the increase in mass over the course of the entire experiment and ΔT is the length of the experiment. Except for stationary experiment 2, starting mass (W1) was estimated rather than measured directly and this was guided by a calibration curve of biomass versus plant surface area.
For comparison with literature sources, a relative growth rate (RGR) was calculated from estimations and measurements of fresh biomass using the formula [47]:
R G R = l n W 3 W 1 Δ T
where ln is the natural log, W1 is starting biomass, W3 is total biomass on day 3 and ΔT is the length of time. As biomass was removed throughout each experiment to maintain a constant plant density, this formula was only used to calculate the RGR up to the first instance of removal (day 3). The total increase in mass over the course of the experiment is presented as the yield.

2.3.2. Chlorophyll a Fluorescence

Chlorophyll a fluorescence measurements were taken for randomly selected plants on days 0 and 7, using a pulse amplitude modulated fluorometer (WALZ Imaging fluorometer, Effeltrich, Germany). The procedure that was followed is detailed in Walsh et al. [32].

2.3.3. Total Nitrogen and Total Phosphorous Analysis

A sample of medium was taken for total nitrogen (TN) and total phosphorous (TP) analysis on the initial and final days of each experiment. For TN analysis, Hach test LCK138 was used with a Hach DR3900 spectrophotometer. Firstly, the sample was digested with peroxo-disulphate for one hour at 100 °C causing inorganically and organically bonded nitrogen to oxidise to nitrate (Koroleff digestion). The resulting oxidised nitrate was then analysed photometrically in a reaction with 2,6-dimethylphenol. For TP analysis, Hach test LCK348 was used. Firstly, the medium was digested using the persulphate digestion method for one hour at 100 °C. The resulting solution was then analysed photometrically through the ascorbic acid/phosphomolybdenum blue method.

2.3.4. Protein Analysis

Lemna minor samples, taken on the final day of experiments, were kept at −20 °C until used for protein extraction and analysis. Protein was extracted using 50 mM potassium phosphate buffer (pH 7, containing 0.1 mM polyvinylpyrrolidone (PVP) and 0.1 mM EDTA). Between 50–80 mg of fresh plant material was homogenised in cold potassium phosphate buffer (1 mL of buffer to 80 mg of plant sample). The homogenised sample was then centrifuged at 20,000× g for 30 min at 4 °C [48]. The resulting supernatant was used for protein analysis using the Bradford method with bovine serum albumin as a standard [49]. For absorbance measurements, 5 µL of sample was added to 1 mL of Bradford reagent in a cuvette and left for five minutes in dark conditions. Absorbance was measured at 595 nm using a spectrophotometer (UV-160A Shimadzu). In order to calculate the proportion of protein based on dry plant biomass, 4% dry weight content of fresh duckweed weight was used [28].

2.4. Data Analysis

Statistical analyses were conducted using R (version 3.4.3 [50]). One- and two-way ANOVAs were used to analyse differences between treatments for the measured parameters. Post hoc Tukey tests were used for pairwise comparisons of treatment groups. Normality was assessed through a graphical assessment of the distribution of the residual values for data points (i.e., histogram). Homoscedasticity was assessed with ‘residuals vs. predicted values’ plots as well as Fligner-Killeen and Levene’s tests.

3. Results

3.1. Stationary Remediation Experiment 1: Growth and Remediation at Variable Plant Densities

The absolute plant biomass yield (g) did not significantly vary over the course of the experiment although the general trend of the average yield increased with increasing density (one-way ANOVA: F(7) = 1.57, p = 0.174; Figure 2a). SGR (d−1) exhibited the opposite trend; rates decreased as density increased (one-way ANOVA: F(7) = 8.357, p < 0.001; Figure 2b). The overall removal of TN (mg) from synthetic dairy wastewater increased as plant density increased (one-way ANOVA: F(7) = 2.574, p < 0.05; Figure 2c). However, when TN removal was expressed per frond surface area (mg N m−2 day−1), the rate decreased as density increased (one-way ANOVA: F(7) = 9.287, p < 0.001; Figure 2d). A similar pattern was found for TP removal in which the overall removal of TP (mg) from synthetic dairy wastewater increased as plant density increased (one-way ANOVA: F(7) = 5.11, p < 0.001; Figure 2e). While the TP removal rate per frond surface area (mg P m−2 day−1) decreased as density increased (one-way ANOVA: F(7) = 8.158, p < 0.001; Figure 2f). There was no difference in protein content (% dry duckweed mass) detected in relation to plant density (one-way ANOVA: F(7) = 0.334, p = 0.933; Figure 2g).

3.2. Stationary Remediation Experiment 2: Growth and Remediation at Low and High Density

On both half-strength Hutner’s and synthetic wastewater L. minor grown at a lower density (25% plant surface coverage) displayed a higher absolute yield (two-way ANOVA: F(1) = 46.607, p < 0.001; Figure 3a) than plants grown at the higher density condition (60% plant surface coverage). The same was found for SGR (two-way ANOVA: F(1) = 45.994, p < 0.001; Figure 3b). TN removal (mg) was not significantly affected by density (two-way ANOVA: F(1) = 3.642, p = 0.0805; Figure 3c), nor was TN removal rate per frond area (mg N m−2 d−1) (two-way ANOVA: F(1) = 3.154, p = 0.101; Figure 3d), although average values were lower at the higher density. Density condition did not significantly affect TP removal (mg) (two-way ANOVA: F(1) = 2.592, p = 0.136; Figure 3e) or TP removal rate per frond area (mg P m−2 d−1) (two-way ANOVA: F(1) = 2.293, p = 0.158; Figure 3f).

3.3. Chlorophyll a Fluorescence

Chlorophyll a fluorescence measurements were taken on the initial day of stationary experiment 2 (day 0, data not shown). They showed that plants grown in both half-strength Hutner’s and synthetic wastewater displayed similar values for a range of chlorophyll fluorescence parameters: Fv/Fm, Y(II), Y(NPQ) and Y(NO) (one-way ANOVAs across all treatments: F(1) = 1.006, 0, 0.049, 0.076, p = 0.354, 0.986, 0.833, 0.792, respectively). Chlorophyll fluorescence measurements taken on the final day (day 7) of the experiment revealed some differences between treatments. Similar to day 0 values, mean Fv/Fm stayed largely constant between 0.68 and 0.8, and was not significantly affected by density (two-way ANOVA: F(1) = 2.038, p = 0.179; Figure 4a) or medium (two-way ANOVA: F(1) = 0.911, p = 0.359; Figure 4a). However, measurements taken on day seven showed that higher plant density in both media resulted in a lower Y(II) (two-way ANOVA: F(1) = 34.054, p < 0.001; Figure 4b). This effect was strongest in plants grown on synthetic wastewater (post hoc Tukey test low:high density in synthetic wastewater: p < 0.001; Figure 4b). Medium alone did not significantly affect Y(II) (two-way ANOVA: F(1) = 0.907, p = 0.360; Figure 4b). There was, however, a significant interaction between density and medium (two-way ANOVA interaction ‘density*medium’: F(1) = 7.369, p < 0.05; Figure 4b). Y(NPQ) was not significantly affected by density (two-way ANOVA: F(1) = 0.594, p = 0.456; Figure 4c) or medium (two-way ANOVA: F(1) = 1.953, p = 0.188; Figure 4c). Nor was Y(NO) significantly affected by density (two-way ANOVA: F(1) = 2.016, p = 0.181; Figure 4d) or medium (two-way ANOVA: F(1) = 2.631, p = 0.131; Figure 4d).

3.4. Re-Circulating Remediation System: Growth and Remediation at Variable Plant Densities

The yield of L. minor in the re-circulating experiment was not significantly affected by the plant density (one-way ANOVA: F(2) = 3.238, p = 0.087; Figure 5a). However, the average biomass yield was lowest at the 20% plant density and increased to a plateau at 50% (post hoc Tukey: p = 0.076; Figure 5a). SGR (d−1) steadily decreased with increasing density (one-way ANOVA: F(2) = 5.143, p = 0.032; post hoc Tukey 20–80: p < 0.05; Figure 5b). Overall, TN removal (mg) from synthetic wastewater was not significantly impacted by density (one-way ANOVA: F(2) = 0.698, p = 0.525; Figure 5c). However, TN removal rate (mg N m−2 day−1) decreased significantly as density increased (one-way ANOVA: F(2) = 5.701, p < 0.05; Figure 5d). TN removal rate values dropped from around 2500 mg N m−2 day−1 at 20% plant surface cover to around 500 mg N m−2 day−1 at 80% (post hoc Tukey: p < 0.05; Figure 5d). Overall, mean TP removal remained at around 17 mg across the three plant densities (one-way ANOVA: F(2) = 0.419, p = 0.670; Figure 5e). TP removal rate (mg P m−2 day−1) decreased with increasing density conditions (one-way ANOVA: F(2) = 29.240, p < 0.001; Figure 5f). Mean TP removal rate dropped from 300 mg P m−2 day−1 at 20% to 75 mg P m−2 day−1 at 80% (post hoc Tukey: p < 0.001; Figure 5f). Protein content (% protein of dry duckweed mass) did not vary between plant densities, with mean values remaining between 17–20% (one-way ANOVA: F(2) = 0.225, p = 0.803; Figure 5g).

4. Discussion

4.1. L. minor Growth on Synthetic Wastewater

RGR values recorded in the stationary experiments (based on the first 3-days of growth; 0.03–0.15 d−1) are at the lower end of the range found in the literature for duckweed grown on wastewater (0.04–0.3 d−1) [24,51,52]. They are also lower than those found for duckweed grown on an optimised medium (0.153–0.519 d−1) [26]. This may, in part, be due to the composition of the medium, as duckweed cultivated on wastewater often results in lower growth rates than obtained on optimised growing medium (compare growth rates in Ziegler et al. [26] with Al-Nozaily et al. [51]). As well as this, the relatively low light intensity of 50 µmol m−2 s−1 used for the stationary experiments may have also contributed to reduced growth rates [53]. Nevertheless, it has been shown that the light saturation point is around 50 µmol m−2 s−1 for L. minor grown on synthetic dairy wastewater [32], although the light saturation point for duckweed on more optimised media is 400–600 µmol m−2 s−1 [44,54]. Commonly used experimental light intensities range from 85 to 130 µmol m−2 s−1 [26,52,55], which are in line with OECD guidelines for duckweed toxicity growth inhibition tests [56]. Although, a higher light intensity of 300 µmol m−2 s−1 was used in the re-circulating experiment this only led to marginally improved growth rates. In both the stationary and re-circulating experiments, SGR decreased with increasing density, which corresponds with general trends noted within the literature [36,37].

4.2. Exploring the Mechanism Underlying Density Dependent Changes in Growth Using Chlorophyll Fluorometry

The negative effect of high density on growth may be a result of greater competition for nutrients [57], a quicker depletion of nutrients [58], or self-shading between colonies [59]. Yet, plant-plant competition has also been related to plant neighbour detection, including plant responses such as shade avoidance, root foraging, and use/induction of chemical defences [60,61]. Therefore, it is possible that L. minor senses the closeness of other plants and switches to a more defensive growth strategy, the trade-off of which is a reduction in growth rate. To explore the mechanism underlying the observed decrease in SGR with increasing plant surface density, plants were grown at lower and higher plant surface densities, but with identical biomass per medium volume (stationary experiment 2). This experiment confirmed the impediment of growth rate at higher plant surface densities. However, as plants at each surface density had access to the same volume of medium, the data imply that the growth impediment was due to factors other than nutrient depletion. Indeed, this point is further confirmed by the observation that similar growth impediments occurred at high plant density irrespective of whether nutrient rich Hutner’s medium or more oligotrophic dairy wastewater was used.
Analysis of photosynthetic parameters in stationary experiment 2, measured after seven days of growth, showed that at high density the photosynthetic quantum yield of PSII, Y(II), was significantly depressed. A reduction in Y(II) for L. minor grown at high density means that the plants were using light energy less efficiently compared to those at a lower plant surface density [62]; an effect which was stronger for plants grown on synthetic wastewater. Y(II), together with Y(NPQ) and Y(NO) account for the partitioning of absorbed light energy in PSII [63], the sum of which equals 1 [64]. Accordingly, a reduction in Y(II) implies a concurrent increase in Y(NPQ) and/or Y(NO). The data reveal non-significant increases in Y(NPQ) at higher plant densities for both media, indicating minor increases in the amount of light energy dissipated in a regulated manner, i.e., through thermal dissipation [63], at these higher densities. Thus, a key finding is a density-dependent decrease in Y(II) which is not matched by clear significant parallel increases in Y(NO) and Y(NPQ), nor a clear effect on Fv/Fm.
Overall, the data indicate that duckweed density can affect aspects of the plant’s metabolism (e.g., carbon assimilation or nitrogen metabolism), rather than having a direct effect on PSII activity (i.e., Fv/Fm). As such, this would indirectly reduce photosynthetic yield (Y(II)) and therefore biomass growth [65]. Previously, Kufel et al. [66] described complex changes in plant morphology in L. minor grown at different plant densities. While Zhang et al. [67] described morphological responses of Spirodela polyrhiza to population density that included decreased frond and root size, as well as increased frond thickness. It is possible that shading is a driver of these changes and induces a shade avoidance response in plants at higher densities [60,61]. Nevertheless, acclimation to shade typically results in increased Y(II) and decreased Y(NPQ) at low measuring light intensities [68,69]. However, the chlorophyll fluorescence data in this study show decreased Y(II) at higher plant densities where shading might potentially have been an issue. Therefore, neither a lack of nutrient nor light supply, two well-advocated explanations, adequately explain the high-density induced impediment of growth. Rather, the data point to plant neighbour detection between Lemna-colonies as the most likely explanation of this [60,61]. At present, touch, volatile organic compounds, chemical exudates and possibly even acoustic signals, have all been associated with neighbour detection [70]. Although it is not known to what extent these apply to duckweed, which tend to produce dense mats in natural habitats [35]. Jang et al. [71] reported that Lemna japonica may release interfering chemicals via its root systems, although the effects were interspecific. Similarly, Bich and Kato-Noguchi [72] reported on interspecific allelopathic signals from Lemna minor. It is less clear how allelopathic signals manifest between neighbouring duckweed colonies of the same species or clone. In some duckweed species high density, or ‘overcrowding’, has been associated with the production of ethylene, a possible early signal for an increasing lack of space which can inhibit growth [73]. Ethylene has been shown in other plant species to an inhibitor of plant growth [74]. Further studies have shown that the overcrowding-stimulated production of ethylene in duckweed is a Ca2+ and phytochrome-dependent process [75,76]. A transient increase in cytoplasmic Ca2+ is followed by an increase in ethylene production [75]. Nevertheless, further exploration is required to understand how plant signals, such as ethylene and other potentially unknown signals, influence L. minor growth and metabolism in high density conditions.

4.3. L. minor Biomass Yield and Protein Content under Variable Density and System Conditions

Given its high protein content [29,77], and usefulness as a biofuel and a source of phytochemicals [28,78], Lemna minor biomass is an important by-product of the wastewater remediation process. As such, the absolute biomass yield is an important parameter, as it relates directly to the amount of plant mass available for further use [27]. Previously, high duckweed densities of 60–80% plant coverage (around 160–280 g m−2) have been reported to result in maximum yields, in combination with different harvesting regimes [39,79,80]. Data from stationary experiment 1 and the re-circulating experiment show that at higher plant densities the logarithmic relationship between plant density and yield will plateau. Consequently, the yield increment becomes smaller. However, significant differences between density treatments were not found. Therefore, a clear benefit of higher plant density for biomass yield was not detected. The second stationary experiment shows the opposite trend between density and yield. In this experiment the use of the same plant biomass, but with different container surface areas, led to both a higher growth rate and overall yield at low density. Thus, the use of a shallower/wider container for a volume of wastewater would improve duckweed yield, i.e., surface area space is an important factor in duckweed yield. However, consideration would have to be given to the impact of algae on this result, as less duckweed cover tends to increase the light availability to algae [81].
Duckweed density did not affect protein content in the stationary or re-circulating systems. Nevertheless, the protein content of 30–35% on a dry weight basis found in this study compares favourably with literature sources, where duckweed protein contents up to 45% of dry weight have been reported [44]. Although, more commonly reported values are between 20–35% [28,82]. Furthermore, this compares relatively well with the commonly used high-protein feed, soybean (33–49% [83]). It should be noted that the use of the Bradford assay with BSA as a standard can underestimate plant protein content when compared to other techniques [84,85], which may affect comparisons with literature sources.
The protein content detected for plants grown in the re-circulating system ranged from 17.5–20% of dry weight, which can be considered a low protein content relative to the published range. This demonstrates that scaling up, and the use of circulatory, non-axenic systems may have unexpected consequences. Starch content was not measured in this study, but, as has been observed in some studies, a lower protein content can result in, or be a result of, higher starch content [27]. If low protein content is a consistent problem for duckweed grown on a large-scale, there are established alternative uses for the biomass that do not depend on the protein content, such as biofuel production [86].

4.4. Remediation of TN and TP by L. minor from Synthetic Wastewater

The trend of decreasing growth with increasing density was reflected in the relationship between TN/TP removal rate and density. In both stationary and re-circulating experiments, the lower removal rates of TN and TP per plant surface area (g m−2 d−1) at higher density conditions show that each duckweed colony is removing less nutrients at higher plant densities than those kept at lower densities. The TN and TP removal rates found in the stationary experiments were on the lower end of the wide range of values recorded in the literature: 124–4400 mg N m−2 d−1 and 14–590 mg P m−2 d−1 [57,82,87,88,89,90]. This can be explained by the lower growth rates observed in these experiments [91], which are likely to be in part due to the low light intensity used [32], the specific medium [23], as well as density effects on L. minor discussed previously. Both TN and TP removal rates from the non-axenic, re-circulating experiment compare well with values from non-axenic systems in literature sources [57,91]. Higher removal rates found in the re-circulating experiment compared to stationary may be mostly explained by the presence of algae and microorganisms, which can account for up to 50–70% of nutrient removal in non-axenic systems [57,88], but also by improved mixing of the medium.
Another important criterium for analysing the effectiveness of a remediation system is the absolute amount of nutrients removed from the remediated wastewater. In stationary experiment 1, a higher density of plants per surface area more effectively takes up nutrients, although the growth and nutrient removal rates per frond are significantly lower [36,37]. As the removal of nutrients started to plateau at higher densities of 50–80%, this suggests that 50–80% represents an optimal duckweed density for dairy wastewater phytoremediation.
There were additional issues encountered in a non-axenic re-circulating system, in which the absolute removal of TN and TP was relatively flat across the three densities. The exact cause of this is uncertain. However, microorganisms were present in substantial amounts at all plant densities in this non-axenic system. Previously, algae were shown to significantly contribute to nutrient removal [57,81], as well as having negative effects on duckweed such as increased decomposition rates [92]. At both high and low density, a high proportion of the absolute removal of TN and TP may be attributed to microorganisms, breaking the direct link between plant growth and nutrient removal (e.g., poor plant growth, but high nutrient removal). Considering wastewater remediation alone, whether remediation is fuelled primarily by duckweed or algae, nutrient removal is the desired outcome. However, if duckweed biomass is to be generated as a valuable by-product for further use, then strong competition for nutrients with non-utilised microorganisms is undesirable. Ideally, the duckweed should be taking up the majority of the nutrients. One way to achieve this is by maximising the duckweed surface density, thereby decreasing algal growth [93]. Overall, these results show that upscaling from laboratory-based, stationary systems to larger scale, recirculatory systems is complex, and that simple extrapolations are not necessarily correct. Accordingly, management of duckweed incubators will need to be informed by the effects of plant density on biomass yield, TN and TP removal, as well as competition with algal species.

5. Conclusions

Lemna minor can be successfully grown on synthetic dairy processing wastewater, opening the perspective to both remediate and valorise such waste, in accordance with the principles of the circular economy. Lemna minor has been shown to produce the best remediation at higher densities (50–80%), even though growth rates and nutrient uptake rates were slowest at these densities. The decrease in growth at high density was linked to a decrease in photosynthetic yield, rather than competition for light or nutrients, which points towards signals from neighbouring colonies as the potential cause of growth restrictions. However, in non-axenic, scaled-up conditions that better reflect an industrial duckweed-based remediation system, the benefits of high density were not as clear. High algal presence led to suppressed duckweed yield and static nutrient removal. Thus, despite the suitability of L. minor for valorisation of dairy processing waste, management of wastewater is subject to both interactions between plant density, yield and nutrient removal, as well as complex upscaling effects.

Author Contributions

É.W., H.K. and M.A.K.J. contributed to the study conception and design. Material preparation, data collection and analysis were performed by É.W., H.K. and S.O.; É.W., N.E.C., S.O., M.A.K.J. and H.K. contributed to the interpretation of results. The first draft of the manuscript was written by É.W.; É.W., N.E.C., S.O., M.A.K.J. and H.K. contributed to writing and editing the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This project was funded under the EPA Research Programme 2014–2020 (grant 2016-W-LS-11). The EPA Research Programme is a Government of Ireland initiative funded by the Department of Communications, Climate Action and Environment. It is administered by the Environmental Protection Agency, which has the statutory function of co-ordinating and promoting environmental research.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used in the current study are available from the corresponding author on reasonable request.

Acknowledgments

MAKJ acknowledges support by WoB.

Conflicts of Interest

The authors have no relevant financial or non-financial interests to disclose.

References

  1. Mehrabi, Z.; Ellis, E.C.; Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 2018, 1, 409–412. [Google Scholar] [CrossRef] [Green Version]
  2. Ranganathan, J.; Vennard, D.; Waite, R.; Dumas, P.; Lipinski, B.; Searchinger, T. Shifting Diets for a Sustainable Food Future; World Resources Institute: Washington, DC, USA, 2016. [Google Scholar]
  3. Nearing, M.A.; Xie, Y.; Liu, B.; Ye, Y. Natural and anthropogenic rates of soil erosion. Int. Soil Water Conserv. Res. 2017, 5, 77–84. [Google Scholar] [CrossRef]
  4. Rosa, L.; Chiarelli, D.D.; Rulli, M.C.; Dell’Angelo, J.; D’Odorico, P. Global agricultural economic water scarcity. Sci. Adv. 2020, 6, eaaz6031. [Google Scholar] [CrossRef] [PubMed]
  5. Pathak, T.B.; Maskey, M.L.; Dahlberg, J.A.; Kearns, F.; Bali, K.M.; Zaccaria, D. Climate Change Trends and Impacts on California Agriculture: A Detailed Review. Agronomy 2018, 8, 25. [Google Scholar] [CrossRef] [Green Version]
  6. Obersteiner, M.; Peñuelas, J.; Ciais, P.; van der Velde, M.; Janssens, I.A. The phosphorus trilemma. Nat. Geosci. 2013, 6, 897–898. [Google Scholar] [CrossRef]
  7. Del Borghi, A.; Moreschi, L.; Gallo, M. Circular economy approach to reduce water–energy–food nexus. Curr. Opin. Environ. Sci. Health 2020, 13, 23–28. [Google Scholar] [CrossRef]
  8. Ghisellini, P.; Cialani, C.; Ulgiati, S. A review on circular economy: The expected transition to a balanced interplay of environmental and economic systems. J. Clean. Prod. 2016, 114, 11–32. [Google Scholar] [CrossRef]
  9. Xue, Y.; Luan, W.; Wang, H.; Yang, Y. Environmental and economic benefits of carbon emission reduction in animal husbandry via the circular economy: Case study of pig farming in Liaoning, China. J. Clean. Prod. 2019, 238, 117968. [Google Scholar] [CrossRef]
  10. Gaucheron, F. Milk and Dairy Products: A Unique Micronutrient Combination. J. Am. Coll. Nutr. 2011, 30, 400S–409S. [Google Scholar] [CrossRef]
  11. OECD-FAO. Dairy and Dairy Products. In OECD-FAO Agricultural Outlook 2020-2029; OECD-FAO Agricultural Outlook; OECD: Rome, Italy, 2020; ISBN 9789264317673. [Google Scholar]
  12. Wang, Y.; Serventi, L. Sustainability of dairy and soy processing: A review on wastewater recycling. J. Clean. Prod. 2019, 237, 117821. [Google Scholar] [CrossRef]
  13. Ince, O. Performance of a two-phase anaerobic digestion system when treating dairy wastewater. Water Res. 1998, 32, 2707–2713. [Google Scholar] [CrossRef]
  14. Demirel, B.; Yenigun, O. Anaerobic acidogenesis of dairy wastewater: The effects of variations in hydraulic retention time with no pH control. J. Chem. Technol. Biotechnol. 2004, 79, 755–760. [Google Scholar] [CrossRef]
  15. Malaspina, F.; Cellamare, C.M.; Stante, L.; Tilche, A. Anaerobic treatment of cheese whey with a downflow-upflow hybrid reactor. Bioresour. Technol. 1996, 55, 131–139. [Google Scholar] [CrossRef]
  16. Ghaly, A.E.; Singh, R.K. Pollution potential reduction of cheese whey through yeast fermentation. Appl. Biochem. Biotechnol. 1989, 22, 181–203. [Google Scholar] [CrossRef]
  17. Ahmad, T.; Aadil, R.M.; Ahmed, H.; ur Rahman, U.; Soares, B.C.V.; Souza, S.L.Q.; Pimentel, T.C.; Scudino, H.; Guimarães, J.T.; Esmerino, E.A.; et al. Treatment and utilization of dairy industrial waste: A review. Trends Food Sci. Technol. 2019, 88, 361–372. [Google Scholar] [CrossRef]
  18. Bunce, J.T.; Ndam, E.; Ofiteru, I.D.; Moore, A.; Graham, D.W. A Review of Phosphorus Removal Technologies and Their Applicability to Small-Scale Domestic Wastewater Treatment Systems. Front. Environ. Sci. 2018, 6, 8. [Google Scholar] [CrossRef] [Green Version]
  19. López-Mosquera, M.E.; Moirón, C.; Carral, E. Use of dairy-industry sludge as fertiliser for grasslands in northwest Spain: Heavy metal levels in the soil and plants. Resour. Conserv. Recycl. 2000, 30, 95–109. [Google Scholar] [CrossRef]
  20. Akansha, J.; Nidheesh, P.V.; Gopinath, A.; Anupama, K.V.; Kumar, M.S. Treatment of dairy industry wastewater by combined aerated electrocoagulation and phytoremediation process. Chemosphere 2020, 253, 126652. [Google Scholar] [CrossRef] [PubMed]
  21. Walsh, É.; Paolacci, S.; Burnell, G.; Jansen, M.A.K. The importance of the calcium-to-magnesium ratio for phytoremediation of dairy industry wastewater using the aquatic plant Lemna minor L. Int. J. Phytoremediat. 2020, 22, 694–702. [Google Scholar] [CrossRef] [PubMed]
  22. Carvalho, F.; Prazeres, A.R.; Rivas, J. Cheese whey wastewater: Characterization and treatment. Sci. Total Environ. 2013, 445, 385–396. [Google Scholar] [CrossRef]
  23. Toyama, T.; Hanaoka, T.; Tanaka, Y.; Morikawa, M.; Mori, K. Comprehensive evaluation of nitrogen removal rate and biomass, ethanol, and methane production yields by combination of four major duckweeds and three types of wastewater effluent. Bioresour. Technol. 2018, 250, 464–473. [Google Scholar] [CrossRef] [PubMed]
  24. Dinh, T.T.U.; Soda, S.; Nguyen, T.A.H.; Nakajima, J.; Cao, T.H. Nutrient removal by duckweed from anaerobically treated swine wastewater in lab-scale stabilization ponds in Vietnam. Sci. Total Environ. 2020, 722, 137854. [Google Scholar] [CrossRef]
  25. Verma, R.; Suthar, S. Synchronized urban wastewater treatment and biomass production using duckweed Lemna gibba L. Ecol. Eng. 2014, 64, 337–343. [Google Scholar] [CrossRef]
  26. Ziegler, P.; Adelmann, K.; Zimmer, S.; Schmidt, C.; Appenroth, K.J. Relative in vitro growth rates of duckweeds (Lemnaceae)—The most rapidly growing higher plants. Plant Biol. 2015, 17, 33–41. [Google Scholar] [CrossRef]
  27. Xu, J.; Cui, W.; Cheng, J.J.; Stomp, A.-M. Production of high-starch duckweed and its conversion to bioethanol. Biosyst. Eng. 2011, 110, 67–72. [Google Scholar] [CrossRef]
  28. Appenroth, K.J.; Sree, K.S.; Böhm, V.; Hammann, S.; Vetter, W.; Leiterer, M.; Jahreis, G. Nutritional value of duckweeds (Lemnaceae) as human food. Food Chem. 2017, 217, 266–273. [Google Scholar] [CrossRef] [PubMed]
  29. Cheng, J.J.; Stomp, A.-M. Growing Duckweed to Recover Nutrients from Wastewaters and for Production of Fuel Ethanol and Animal Feed. Clean Soil Air Water 2009, 37, 17–26. [Google Scholar] [CrossRef]
  30. de Beukelaar, M.F.A.; Zeinstra, G.G.; Mes, J.J.; Fischer, A.R.H. Duckweed as human food. The influence of meal context and information on duckweed acceptability of Dutch consumers. Food Qual. Prefer. 2019, 71, 76–86. [Google Scholar] [CrossRef]
  31. Stadtlander, T.; Förster, S.; Rosskothen, D.; Leiber, F. Slurry-grown duckweed (Spirodela polyrhiza) as a means to recycle nitrogen into feed for rainbow trout fry. J. Clean. Prod. 2019, 228, 86–93. [Google Scholar] [CrossRef]
  32. Walsh, É.; Kuehnhold, H.; O’Brien, S.; Coughlan, N.E.; Jansen, M.A.K. Light intensity alters the phytoremediation potential of Lemna minor. Environ. Sci. Pollut. Res. 2021. [Google Scholar] [CrossRef] [PubMed]
  33. Kushwaha, J.P.; Srivastava, V.C.; Mall, I.D. Sequential batch reactor for dairy wastewater treatment: Parametric optimization; kinetics and waste sludge disposal. J. Environ. Chem. Eng. 2013, 1, 1036–1043. [Google Scholar] [CrossRef]
  34. Charalambous, P.; Shin, J.; Shin, S.G.; Vyrides, I. Anaerobic digestion of industrial dairy wastewater and cheese whey: Performance of internal circulation bioreactor and laboratory batch test at pH 5–6. Renew. Energy 2020, 147, 1–10. [Google Scholar] [CrossRef]
  35. Landolt, E. Biosystematic Investigations in the Family of Duckweeds (Lemnaceae). The Family of Lemnaceae—A Monographic Study, Volume 1; Veröffentlichungen des Geobotanischen Institutes der ETH: Zürich, Switzerland, 1986. [Google Scholar]
  36. Driever, S.M.; van Nes, E.H.; Roijackers, R.M.M. Growth limitation of Lemna minor due to high plant density. Aquat. Bot. 2005, 81, 245–251. [Google Scholar] [CrossRef]
  37. Frédéric, M.; Samir, L.; Louise, M.; Abdelkrim, A. Comprehensive modeling of mat density effect on duckweed (Lemna minor) growth under controlled eutrophication. Water Res. 2006, 40, 2901–2910. [Google Scholar] [CrossRef] [PubMed]
  38. Ceschin, S.; Sgambato, V.; Ellwood, N.T.W.; Zuccarello, V. Phytoremediation performance of Lemna communities in a constructed wetland system for wastewater treatment. Environ. Exp. Bot. 2019, 162, 67–71. [Google Scholar] [CrossRef]
  39. Xu, J.; Shen, G. Growing duckweed in swine wastewater for nutrient recovery and biomass production. Bioresour. Technol. 2011, 102, 848–853. [Google Scholar] [CrossRef]
  40. Xu, J.; Zhao, H.; Stomp, A.-M.; Cheng, J.J. The production of duckweed as a source of biofuels. Biofuels 2012, 3, 589–601. [Google Scholar] [CrossRef]
  41. Lahive, E.; O’Halloran, J.; Jansen, M.A.K. Differential sensitivity of four Lemnaceae species to zinc sulphate. Environ. Exp. Bot. 2011, 71, 25–33. [Google Scholar] [CrossRef]
  42. Hutner, S.H. Comparative physiology of heterotrophic growth in higher plants. In Growth and Differentiation in Plants; Iowa State College Press: Ames, IA, USA, 1953; pp. 417–447. [Google Scholar]
  43. Tarpey, E. An Investigation into the Use of IASBRs for Treatment of Dairy Processing Wastewater. Master’ Thesis, National University of Ireland, Galway, Ireland, 2016. [Google Scholar]
  44. Landolt, E.; Kandeler, R. Biosystematic Investigations in the Family of Duckweeds (Lemnaceae). The Family of Lemnaceae—A Monographic Study, Volume 2; Veröffentlichungen des Geobotanischen Institutes der ETH: Zürich, Switzerland, 1987. [Google Scholar]
  45. Easlon, H.M.; Bloom, A.J. Easy Leaf Area: Automated digital image analysis for rapid and accurate measurement of leaf area. Appl. Plant Sci. 2014, 2, 1400033. [Google Scholar] [CrossRef]
  46. Chaiprapat, S.; Cheng, J.J.; Classen, J.J.; Liehr, S.K. Role of internal nutrient storage in duckweed growth for swine wastewater treatment. Trans. ASAE 2005, 48, 2247–2258. [Google Scholar] [CrossRef]
  47. Connolly, J.; Wayne, P. Asymmetric competition between plant species. Oecologia 1996, 108, 311–320. [Google Scholar] [CrossRef] [PubMed]
  48. Balen, B.; Tkalec, M.; Šikić, S.; Tolić, S.; Cvjetko, P.; Pavlica, M.; Vidaković-Cifrek, Ž. Biochemical responses of Lemna minor experimentally exposed to cadmium and zinc. Ecotoxicology 2011, 20, 815–826. [Google Scholar] [CrossRef]
  49. Bradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 1976, 72, 248–254. [Google Scholar] [CrossRef]
  50. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019. [Google Scholar]
  51. Al-Nozaily, F.; Alaerts, G.; Veenstra, S. Performance of duckweed-covered sewage lagoons—II. Nitrogen and phosphorus balance and plant productivity. Water Res. 2000, 34, 2734–2741. [Google Scholar] [CrossRef]
  52. Iatrou, E.I.; Stasinakis, A.S.; Aloupi, M. Cultivating duckweed Lemna minor in urine and treated domestic wastewater for simultaneous biomass production and removal of nutrients and antimicrobials. Ecol. Eng. 2015, 84, 632–639. [Google Scholar] [CrossRef]
  53. Paolacci, S.; Harrison, S.; Jansen, M.A.K. The invasive duckweed Lemna minuta Kunth displays a different light utilisation strategy than native Lemna minor Linnaeus. Aquat. Bot. 2018, 146, 8–14. [Google Scholar] [CrossRef]
  54. Wedge, R.M.; Burris, J.E. Effects of light and temperature on duckweed photosynthesis. Aquat. Bot. 1982, 13, 133–140. [Google Scholar] [CrossRef]
  55. Al-Nozaily, F.; Alaerts, G.; Veenstra, S. Performance of duckweed-covered sewage lagoons—I. Oxygen balance and COD removal. Water Res. 2000, 34, 2727–2733. [Google Scholar] [CrossRef]
  56. OECD. Test No. 221: Lemna sp. Growth Inhibition Test; OECD Guidelines for the Testing of Chemicals, Section 2; OECD: Paris, France, 2006. [Google Scholar]
  57. Körner, S.; Vermaat, J.E. The relative importance of Lemna gibba L., bacteria and algae for the nitrogen and phosphorus removal in duckweed-covered domestic wastewater. Water Res. 1998, 32, 3651–3661. [Google Scholar] [CrossRef]
  58. Porath, D.; Hepher, B.; Koton, A. Duckweed as an aquatic crop: Evaluation of clones for aquaculture. Aquat. Bot. 1979, 7, 273–278. [Google Scholar] [CrossRef]
  59. Debusk, T.A.; Ryther, J.H.; Hanisak, M.D.; Williams, L.D. Effects of seasonality and plant density on the productivity of some freshwater macrophytes. Aquat. Bot. 1981, 10, 133–142. [Google Scholar] [CrossRef]
  60. Kong, C.-H.; Zhang, S.-Z.; Li, Y.-H.; Xia, Z.-C.; Yang, X.-F.; Meiners, S.J.; Wang, P. Plant neighbor detection and allelochemical response are driven by root-secreted signaling chemicals. Nat. Commun. 2018, 9, 3867. [Google Scholar] [CrossRef] [Green Version]
  61. Pierik, R.; Mommer, L.; Voesenek, L.A.C.J. Molecular mechanisms of plant competition: Neighbour detection and response strategies. Funct. Ecol. 2013, 27, 841–853. [Google Scholar] [CrossRef]
  62. Murchie, E.H.; Lawson, T. Chlorophyll fluorescence analysis: A guide to good practice and understanding some new applications. J. Exp. Bot. 2013, 64, 3983–3998. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Klughammer, C.; Schreiber, U. Complementary PS II quantum yields calculated from simple fluorescence parameters measured by PAM fluorometry and the Saturation Pulse method. PAM Appl. Notes 2008, 1, 27–35. [Google Scholar]
  64. Kramer, D.M.; Johnson, G.; Kiirats, O.; Edwards, G.E. New fluorescence parameters for the determination of QA redox state and excitation energy fluxes. Photosynth. Res. 2004, 79, 209–218. [Google Scholar] [CrossRef] [PubMed]
  65. Juneau, P.; Qiu, B.; Deblois, C.P. Use of chlorophyll fluorescence as a tool for determination of herbicide toxic effect: Review. Toxicol. Environ. Chem. 2007, 89, 609–625. [Google Scholar] [CrossRef]
  66. Kufel, L.; Strzałek, M.; Przetakiewicz, A. Plant response to overcrowding–Lemna minor example. Acta Oecologica 2018, 91, 73–80. [Google Scholar] [CrossRef]
  67. Zhang, L.-M.; Jin, Y.; Yao, S.-M.; Lei, N.-F.; Chen, J.-S.; Zhang, Q.; Yu, F.-H. Growth and Morphological Responses of Duckweed to Clonal Fragmentation, Nutrient Availability, and Population Density. Front. Plant Sci. 2020, 11, 618. [Google Scholar] [CrossRef] [PubMed]
  68. Huang, D.; Wu, L.; Chen, J.R.; Dong, L. Morphological plasticity, photosynthesis and chlorophyll fluorescence of Athyrium pachyphlebium at different shade levels. Photosynthetica 2011, 49, 611–618. [Google Scholar] [CrossRef]
  69. Hallik, L.; Niinemets, Ü.; Kull, O. Photosynthetic acclimation to light in woody and herbaceous species: A comparison of leaf structure, pigment content and chlorophyll fluorescence characteristics measured in the field. Plant Biol. 2012, 14, 88–99. [Google Scholar] [CrossRef] [PubMed]
  70. Bilas, R.D.; Bretman, A.; Bennett, T. Friends, neighbours and enemies: An overview of the communal and social biology of plants. Plant. Cell Environ. 2021, 44, 997–1013. [Google Scholar] [CrossRef]
  71. Jang, M.-H.; Ha, K.; Takamura, N. Reciprocal allelopathic responses between toxic cyanobacteria (Microcystis aeruginosa) and duckweed (Lemna japonica). Toxicon 2007, 49, 727–733. [Google Scholar] [CrossRef]
  72. Bich, T.T.N.; Kato-Noguchi, H. Allelopathic potential of two aquatic plants, duckweed (Lemna minor L.) and water lettuce (Pistia stratiotes L.), on terrestrial plant species. Aquat. Bot. 2012, 103, 30–36. [Google Scholar] [CrossRef]
  73. Färber, E.; Königshofer, H.; Kandeler, R. Ethylene Production and Overcrowding in Lemnaceae. J. Plant Physiol. 1986, 124, 379–384. [Google Scholar] [CrossRef]
  74. Dubois, M.; Van den Broeck, L.; Inzé, D. The Pivotal Role of Ethylene in Plant Growth. Trends Plant Sci. 2018, 23, 311–323. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Färber, E.; Kandeler, R. Significance of Calcium Ions in the Overcrowding Effect in Spirodela Polyrrhiza P 143. J. Plant Physiol. 1989, 135, 94–98. [Google Scholar] [CrossRef]
  76. Färber, E.; Kandeler, R. Phytochrome effect on ethylene production after overcrowding in Spirodela (Lemnaceae). Phyton Ann. Rei Bot. Austria 1990, 30, 89–95. [Google Scholar]
  77. Anderson, K.E.; Lowman, Z.; Stomp, A.M.; Chang, J. Duckweed as a Feed Ingredient in Laying Hen Diets and its Effect on Egg Production and Composition. Int. J. Poult. Sci. 2011, 10, 4–7. [Google Scholar] [CrossRef] [Green Version]
  78. Ge, X.; Zhang, N.; Phillips, G.C.; Xu, J. Growing Lemna minor in agricultural wastewater and converting the duckweed biomass to ethanol. Bioresour. Technol. 2012, 124, 485–488. [Google Scholar] [CrossRef]
  79. Verma, R.; Suthar, S. Impact of density loads on performance of duckweed bioreactor: A potential system for synchronized wastewater treatment and energy biomass production. Environ. Prog. Sustain. Energy 2015, 34, 1596–1604. [Google Scholar] [CrossRef]
  80. Xu, J.; Shen, G. Effects of Harvest Regime and Water Depth on Nutrient Recovery from Swine Wastewater by Growing Spirodela oligorrhiza. Water Environ. Res. 2011, 83, 2049–2056. [Google Scholar] [CrossRef] [PubMed]
  81. Roijackers, R.; Szabó, S.; Scheffer, M. Experimental analysis of the competition between algae and duckweed. Arch. Hydrobiol. 2004, 160, 401–412. [Google Scholar] [CrossRef]
  82. Mohedano, R.A.; Costa, R.H.R.; Tavares, F.A.; Belli Filho, P. High nutrient removal rate from swine wastes and protein biomass production by full-scale duckweed ponds. Bioresour. Technol. 2012, 112, 98–104. [Google Scholar] [CrossRef] [Green Version]
  83. Hymowitz, T.; Collins, F.I.; Panczner, J.; Walker, W.M. Relationship between the content of oil, protein, and sugar in soybean seed. Agron. J. 1972, 64, 613–616. [Google Scholar] [CrossRef]
  84. Rekowski, A.; Langenkämper, G.; Dier, M.; Wimmer, M.A.; Scherf, K.A.; Zörb, C. Determination of soluble wheat protein fractions using the Bradford assay. Cereal Chem. 2021. [Google Scholar] [CrossRef]
  85. Mæhre, H.K.; Dalheim, L.; Edvinsen, G.K.; Elvevoll, E.O.; Jensen, I.-J. Protein Determination—Method Matters. Foods 2018, 7, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Cui, W.; Cheng, J.J. Growing duckweed for biofuel production: A review. Plant Biol. 2015, 17, 16–23. [Google Scholar] [CrossRef]
  87. Zimmo, O.R.; van der Steen, N.P.; Gijzen, H.J. Nitrogen mass balance across pilot-scale algae and duckweed-based wastewater stabilisation ponds. Water Res. 2004, 38, 913–920. [Google Scholar] [CrossRef] [PubMed]
  88. Zhao, Y.; Fang, Y.; Jin, Y.; Huang, J.; Ma, X.; He, K.; He, Z.; Wang, F.; Zhao, H. Microbial community and removal of nitrogen via the addition of a carrier in a pilot-scale duckweed-based wastewater treatment system. Bioresour. Technol. 2015, 179, 549–558. [Google Scholar] [CrossRef]
  89. Benjawan, L.; Koottatep, T. Nitrogen removal in recirculated duckweed ponds system. Water Sci. Technol. 2007, 55, 103–110. [Google Scholar] [CrossRef] [PubMed]
  90. Cheng, J.; Landesman, L.; Bergmann, B.; Classen, J.J.; Howard, J.W.; Yamamoto, Y.T. Nutrient Removal from swine lagoon liquid by Lemna minor 8627. Trans. ASAE 2002, 45, 1003–1010. [Google Scholar] [CrossRef]
  91. Cheng, J.; Bergmann, B.A.; Classen, J.J.; Stomp, A.M.; Howard, J.W. Nutrient recovery from swine lagoon water by Spirodela punctata. Bioresour. Technol. 2002, 81, 81–85. [Google Scholar] [CrossRef]
  92. Szabó, S.; Braun, M.; Nagy, P.; Balázsy, S.; Reisinger, O. Decomposition of duckweed (Lemna gibba) under axenic and microbial [-2pt] conditions: Flux of nutrients between litter water and sediment, the impact of leaching and microbial degradation. Hydrobiologia 2000, 434, 201–210. [Google Scholar] [CrossRef]
  93. Szabó, S.; Braun, M.; Borics, G. Elemental flux between algae and duckweeds (Lemna gibba) during competition. Arch. Hydrobiol. 1999, 146, 355–367. [Google Scholar] [CrossRef]
Plants 10 01366 g001 550
Figure 1. Set-up of stationary remediation experiments 1 and 2.
Figure 1. Set-up of stationary remediation experiments 1 and 2.
Plants 10 01366 g001
Plants 10 01366 g002 550
Figure 2. Mean (±SE) values with natural log trendline of (a) yield (g), (b) specific growth rate (SGR, d−1), (c) total nitrogen (TN) removal (mg N), (d) TN removal rate per frond surface area (mg N m−2 d−1), (e) total phosphorous (TP) removal (mg P), (f) TP removal rate per frond surface area (mg P m−2 d−1) and (g) % protein content based on fresh duckweed biomass, FW, and dry duckweed biomass, DW, for L. minor grown on synthetic wastewater over 7 days under a range of plant surface covers (10–80%). Points that do not share the same letter significantly differ from one another, as per the Tukey post hoc test, p < 0.05.
Figure 2. Mean (±SE) values with natural log trendline of (a) yield (g), (b) specific growth rate (SGR, d−1), (c) total nitrogen (TN) removal (mg N), (d) TN removal rate per frond surface area (mg N m−2 d−1), (e) total phosphorous (TP) removal (mg P), (f) TP removal rate per frond surface area (mg P m−2 d−1) and (g) % protein content based on fresh duckweed biomass, FW, and dry duckweed biomass, DW, for L. minor grown on synthetic wastewater over 7 days under a range of plant surface covers (10–80%). Points that do not share the same letter significantly differ from one another, as per the Tukey post hoc test, p < 0.05.
Plants 10 01366 g002
Plants 10 01366 g003 550
Figure 3. Mean (±SE) (a) yield (g), (b) SGR (d−1), (c) TN removal (mg N), (d) TN removal rate per frond surface area (mg N m−2 d−1), (e) TP removal (mg P) and (f) TP removal rate per frond surface area (mg P m−2 d−1), for L. minor grown on synthetic wastewater or half-strength Hutner’s medium at two plant surface covers (low, 25% and high, 60%) over 7 days. A hash symbol (#) denotes an effect of density for p < 0.05, as per the two-way ANOVA.
Figure 3. Mean (±SE) (a) yield (g), (b) SGR (d−1), (c) TN removal (mg N), (d) TN removal rate per frond surface area (mg N m−2 d−1), (e) TP removal (mg P) and (f) TP removal rate per frond surface area (mg P m−2 d−1), for L. minor grown on synthetic wastewater or half-strength Hutner’s medium at two plant surface covers (low, 25% and high, 60%) over 7 days. A hash symbol (#) denotes an effect of density for p < 0.05, as per the two-way ANOVA.
Plants 10 01366 g003
Plants 10 01366 g004 550
Figure 4. Mean (±SE) (a) Fv/Fm, (b) Y(II), (c) Y(NPQ) and (d) Y(NO), for L. minor grown on either synthetic wastewater or half-strength Hutner’s medium at two plant surface covers (low, 25% and high, 60%) over 7 days. A hash symbol (#) denotes an effect of density for p < 0.05, and a star symbol (*) denotes an interactive effect between density and medium for p < 0.05, as per the two-way ANOVA.
Figure 4. Mean (±SE) (a) Fv/Fm, (b) Y(II), (c) Y(NPQ) and (d) Y(NO), for L. minor grown on either synthetic wastewater or half-strength Hutner’s medium at two plant surface covers (low, 25% and high, 60%) over 7 days. A hash symbol (#) denotes an effect of density for p < 0.05, and a star symbol (*) denotes an interactive effect between density and medium for p < 0.05, as per the two-way ANOVA.
Plants 10 01366 g004
Plants 10 01366 g005 550
Figure 5. Mean (±SE) (a) yield (g), (b) SGR (d−1), (c) TN removal (mg N), (d) TN removal rate per frond surface area (mg N m−2 d−1), (e) TP removal (mg P), (f) TP removal rate per frond surface area (mg P m−2 d−1) and (g) % protein content based on fresh duckweed biomass, FW, and dry duckweed biomass, DW, for L. minor grown on synthetic wastewater in a re-circulating system at three plant surface covers (20, 50 and 80 %) over 5 days. Points that do not share the same letter significantly differ from one another, as per the Tukey post hoc test, p < 0.05.
Figure 5. Mean (±SE) (a) yield (g), (b) SGR (d−1), (c) TN removal (mg N), (d) TN removal rate per frond surface area (mg N m−2 d−1), (e) TP removal (mg P), (f) TP removal rate per frond surface area (mg P m−2 d−1) and (g) % protein content based on fresh duckweed biomass, FW, and dry duckweed biomass, DW, for L. minor grown on synthetic wastewater in a re-circulating system at three plant surface covers (20, 50 and 80 %) over 5 days. Points that do not share the same letter significantly differ from one another, as per the Tukey post hoc test, p < 0.05.
Plants 10 01366 g005
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Walsh, É.; Coughlan, N.E.; O’Brien, S.; Jansen, M.A.K.; Kuehnhold, H. Density Dependence Influences the Efficacy of Wastewater Remediation by Lemna minor. Plants 2021, 10, 1366. https://doi.org/10.3390/plants10071366

AMA Style

Walsh É, Coughlan NE, O’Brien S, Jansen MAK, Kuehnhold H. Density Dependence Influences the Efficacy of Wastewater Remediation by Lemna minor. Plants. 2021; 10(7):1366. https://doi.org/10.3390/plants10071366

Chicago/Turabian Style

Walsh, Éamonn, Neil E. Coughlan, Seán O’Brien, Marcel A. K. Jansen, and Holger Kuehnhold. 2021. "Density Dependence Influences the Efficacy of Wastewater Remediation by Lemna minor" Plants 10, no. 7: 1366. https://doi.org/10.3390/plants10071366

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop