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Article

Phycoremediation of Landfill Leachate with Desmodesmus subspicatus: A Pre-Treatment for Reverse Osmosis

by
Marina Kholomyeva
*,
Radek Vurm
,
Lucia Tajnaiová
,
Marek Šír
,
Mariya Maslova
and
Vladimír Kočí
*
Faculty of Environmental technology, Department of Environmental Chemistry, UCT Prague, Technická 5, 166 28 Prague, Czech Republic
*
Authors to whom correspondence should be addressed.
Water 2020, 12(6), 1755; https://doi.org/10.3390/w12061755
Submission received: 3 June 2020 / Revised: 17 June 2020 / Accepted: 18 June 2020 / Published: 20 June 2020
(This article belongs to the Section Wastewater Treatment and Reuse)
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Abstract

:
Reverse osmosis is widely used as one of the most effective and advanced technologies for the treatment of leachate from landfill sites. Unfortunately, high leachate contamination—above all, ammonia nitrogen—affects membrane selectivity and is reflected in permeate quality. Furthermore, iron contained in leachate can facilitate chelates forming, which reduces the membrane anti-fouling capacity. The addition of a pre-treatment step could alleviate the adverse impact of the pollutants. As such, we investigated pollutant removal by phycoremediation. Initial ecotoxicity tests of three algal strains (Scenedesmus obliquus (S. obliquus), Desmodesmus subspicatus (D. subspicatus), and Chlorella vulgaris (C. vulgaris)) identified D. subspicatus as the strain most tolerant to leachate toxicity. Subsequently, D. subspicatus was cultivated in six landfill leachates of different origin and, after the cultivation, removal rates were determined for ammonia nitrogen and iron. Furthermore, the impact of input leachate parameters on remediation efficiency was also investigated. By phycoremediation, the reduction of up to 100% in iron and 83% in ammonia nitrogen load was achieved, which demonstrates the high potential of microalgae to mitigate environmental risks and reduce membrane foulant content.

Graphical Abstract

1. Introduction

Historically, landfilling is the most common solid waste management practice, with almost 40% of global waste disposed of in this way [1,2]. It is generally considered the final stage in the waste disposal hierarchy due to the loss of valuable energy resources and the risk of gas and landfill leachate (LL) emissions. LL is one of the most difficult wastewaters (WW) to treat because of its varied composition, particularly its high organic and total ammonia nitrogen (TAN) load [1,3]. Reverse osmosis (RO) is widely used as one of the most advanced LL treatment approaches [4], but several factors can affect its efficiency.
One of the main factors affecting RO efficiency is the high concentration of TAN in LL. The reason for this is that nitrogen in the form of ammonium is released from the waste through protein degradation and, due to its stable nature under anaerobic conditions, subsequently accumulates in the LL [5]. The consequent pH increase in the LL shifts the NH4+/NH3 equilibrium in favor of the NH3 molecules, thereby affecting membrane selectivity. In turn, this leads to the loss of NH4+–N from the concentrate and affects the quality of the permeate [6].
Another limiting factor in the application of RO is fouling due to the clogging of pores or adsorption of solutes on the membrane surface. The fouling of RO membranes is mainly caused by organic compounds, such as humic acids, fulvic acids, and humins. In mature municipal landfills in particular, their concentration can reach up to 60% of the total organic carbon (TOC) [7]. These foulants are known for their tendency to form complexes or chelates with metal ions, which leads to a remarkable increase in the solubility of the metals. Iron is one of the most common metals responsible for membrane pore clogging. As well as chelates forming, iron generates oxides and hydroxides that also affect membrane fouling [8,9]. The fouling of RO membranes is reflected in a decline in process efficiency and increased costs.
To reduce NH4+–N loss from the concentrate and protect RO membranes against fouling, an LL pre-treatment step may be considered. In the literature, several approaches for LL pre-treatment prior to membrane separation can be found. For example, Zakar et al. [10] employed Fenton oxidation and ozonization to treat tannery wastewater rich in organic foulants. Kim et al. evaluated the feasibility of cold-cathode X-ray irradiation as a fouling control technique prior to the RO of shale gas produced water [11]. To remove RO foulants from secondary-treated municipal effluent, Wang et al. [12] investigated the combination of a full-scale biological aerated filter, coagulation, sand filtration, and ultrafiltration. However, to minimize the environmental footprint of pre-RO treatment, the implementation of an eco-friendly method is desirable, with phycoremediation appearing to be a promising approach.
Phycoremediation harnesses the ability of microalgae to accumulate nutrients and selected pollutants from WW, thereby increasing algal biomass. The ability of microalgae to remove contaminants from various types of WW has been described in numerous studies. The phycoremediation process has been successfully applied to treat municipal WW [13], agricultural effluent [14], and various types of industrial WW, such as tannery WW [15], dairy industry effluents [16], and distillery WW [17]. De Alva et al. [18] reported that during the cultivation of Scenedesmus acutus in pre-treated municipal water, TOC decreased by up to 77%, and phosphate and TAN load by up to 65% and 92%, respectively. In the same type of water, a reduction in total nitrogen and phosphorus of 74% and 90%, respectively, was also observed in the cultivation of the marine microalga Nannochloropsis oculata [19].
Many microalgal strains have also demonstrated the potential to remove heavy metals from WW. The removal of heavy metals by microalgae is attained via two mechanisms. The first is rapid extracellular passive adsorption (biosorption), while the other is slow intracellular diffusion and the accumulation of metals (bioaccumulation). It has been found that the great potential of green algae to adsorb metals from water owes to their large surface area and the high binding ability of their functional groups (such as carboxyl, hydroxyl, phosphate, thiol, and others) [20,21]. Gao et al. [22] showed that through the cultivation of C. vulgaris in domestic secondary effluent, high removal efficiencies could be achieved for Cu, Zn, Fe, Al, and Mn (65%, 80%, 100%, 93%, and 100%, respectively). By cultivating Scenedesmus quadricauda in WW, 65% of Cd and 69% of Pb were removed [23].
Due to the phenomenon called hormesis, low concentrations of heavy metals can enhance microalgal biomass growth by stimulating cell metabolic processes. Moreover, some heavy metals, including copper (Cu), manganese (Mn), zinc (Zn), and iron (Fe), are essential for green microalgal cell metabolism, as they enable vital intracellular biochemical mechanisms. Therefore, microalgae uptake these metals from water as micronutrients [24]. By contrast, in higher concentrations, these metals and other non-essential metals are toxic for microalgae as they can cause the formation of reactive oxygen species (ROS), such as peroxides, superoxides, hydroxyl radicals, and singlet oxygen. These oxidants interact with intracellular lipids, proteins, and nucleic acids, resulting in their damage and causing oxidative stress.
As a self-defense response, microalgae cells synthetize enzymes (catalase, ascorbate peroxidase, glutathione reductase, and others) or non-enzymatic antioxidant agents, such as carotenoids, glutathione, and cysteine, which break down the free radicals to less reactive compounds. Another cell defense mechanism of microalgae consists of the intracellular synthesis of chelate agents such as phytochelatin. Those compounds make bonds with metal ions, thereby preventing the interaction of the ions with intracellular macromolecules by forming chelate–metal coordination complexes [24,25,26]. Oxidative stress is used as a stimulating mechanism in target compound production; for example, lipid accumulation for biodiesel [27]. To date, however, no study has investigated the applicability of phycoremediation as a pre-RO step in LL treatment.
The major aim of this work was to investigate the ability of green microalgae to remove selected pollutants from LL, which promote membrane fouling and reduce the permeate quality. Here, we present the results of growing green microalgae in LLs under laboratory conditions to assess the efficiency of phycoremediation. Three microalgal strains (D. subspicatus, C. vulgaris, and S. obliquus) were compared in this study to investigate their general ability to grow in LL. We also investigated the removal efficiency of selected pollutants by phycoremediation depending on the input concentration of the pollutants.

2. Materials and Methods

2.1. Regions Selected for Sampling

Leachate composition greatly depends on the landfilled waste composition [28,29]. We chose two regions, Ghana and Czechia, assuming that TOC input concentration would be considerably higher in LLs from Ghana than those from Czechia, whose waste management strategy aims to ban the landfilling of mixed municipal waste and recyclable or recoverable waste [29].
In Ghana, on the contrary, landfilling remains a predominant municipal waste disposal option. Landfills in Ghana are essentially dumpsites created in abandoned quarries [30]. Organic matter is the major compound of municipal solid waste with an average generation rate of up to 0.29 kg per person daily [31]. In this study, we compared landfill leachates from Ghana and Czechia to investigate the ability of microalgae to adapt to various concentrations of TAN and TOC load. We also investigated pollutant removal efficiency depending on the input concentration.

2.2. Landfill Leachates

The samples of LLs were obtained from six municipal solid waste landfills, three located in Ghana and three in Czechia. For confidentiality reasons, the landfills are not named and are referred to as A, B, and C (Czechia) and D, E, and F (Ghana). The characteristics of the landfills are given in Table 1 [32,33]. The samples from Ghana were collected during the dry season, which south Ghana experiences from mid-November to February. The samples were stored in dark glass bottles without headspace, and transported to Czechia within 48 h after sampling. The samples from Czechia were collected in 100 L plastic barrels and immediately transported to the laboratory. On arrival at the laboratory, required amounts of the samples were taken and immediately processed for analyses. The physical characteristics of the leachates—temperature, pH, and electrical conductivity—were measured using the Standard Methods for the Examination of Water and Wastewater [34]. Fe, TAN, and TOC concentrations (mg·L−1) were also measured in the samples.

2.3. Microalgae and Growth Medium

In this work, three microalgal strains were used. D. subspicatus (Brinkmann 1953/SAG), C. vulgaris (Beijerinck), and S. obliquus (Turpin Kuetzing) strains were purchased from the Culture Collection of Autotrophic Organisms (CCALA) of the Institute of Botany of the Czech Academy of Sciences, Czechia. Sterilized Bischoff and Bold medium (BBM) [35] with pH 6.6 was used as the growth and control medium.

2.4. Experimental Set-up

The experiment was set up to investigate pollutant removal by growing the microalgae in the LL. All samples were prepared in triplicate; control samples, in duplicate. The samples were handled in a flowbox to maintain sterile conditions. The LL samples were centrifuged at 10,000 rpm for 10 min and subsequently filtered through a 0.45 µm filter. The initial density of algal inoculum was about 100,000 cells·mL−1. Cultivation was performed in 100 mL Erlenmeyer flasks containing 50 mL test samples (LLs + inoculum) in an incubator with fluorescent illumination at 10,000 lux with a dark/light period of 16:8 h. Chamber temperature was maintained at 23 °C. Biomass concentration was monitored every 48 ± 2 h by spectrophotometric measurement of the optical density (OD). Culturing was conducted until the density of the algal culture decreased below the highest value measured in the exponential phase. The OD was correlated to cell concentration measured with a Bürker counting chamber at the declining relative growth phase [36]. For this study, we preferred OD measurement to suspended solid measurement, due to small flasks’ bulk and low absolute biomass weight, which might have induced significant errors in the results. Each recorded OD was corrected by taking away that of the corresponding blank sample.
The experiment was divided into two parts. As a first step, an initial ecotoxicity test of the three algal strains was conducted, to verify a general ability of microalgae to grow in LL, and to select the most suitable algal culture for application in further tests. For this purpose, two landfill leachates, A (Czechia) and D (Ghana), diluted with distilled water to a concentration of 20%, were used in this test. The selection of the particular leachates was conditioned by their input characteristics, as we intended to compare two leachates of different origin but similar color and pH, TOC, total inorganic carbon (TIC), total carbon (TC), and N:P values, thereby minimizing the influence of the particular parameter on microalgae growth (Table 2). The incubation period in the selected leachates lasted for 11 days for C. vulgaris, 24 days for D. subspicatus, and 21 days for S. obliquus.
As a second step, the selected algal strain (D. subspicatus) was cultured in various concentrations of all LLs to investigate its potential for pollutant removal. In lab-scale studies of the phycoremediation process, LLs of various dilutions are usually used, with the dilution factor in the range of 10–100% [37,38,39]. The effect of dilution can mitigate the toxicity of LL and boost microalgal growth. In the present study, the LLs were diluted to concentrations of 10%, 20%, and 50%, using distilled water. Depending on the particular sample, the total incubation period lasted 8 to 38 days.

2.5. Analytical Measurements

  • Optical density measurement. During cultivation, the microalgae concentration was monitored through OD using the SHIMADZU UV-1900 spectrophotometer (Shimadzu Scientific Instrument, Inc., Columbia, MD, USA) at λ = 560 nm under sterile conditions.
  • TOC analysis. An Elementar Vario TOC analyzer (Elementar Analysensysteme, GmbH, Langenselbold, Germany) was used to measure TOC content. For the analysis of each sample, 1 mL of the supernatant was taken out and diluted to 10% with distilled water. The TOC was determined by measuring TC and TIC separately, and calculating their difference.
  • Fe analysis. Fe content was measured through atomic absorption spectrometry at a specific wavelength. To determine Fe content decrease, a SensAA atomic absorption spectrometer (GBC Scientific Equipment, Melbourne, Australia) was used.
  • TAN analysis. TAN concentration was determined using the phenate method [40]. The absorbance was measured with the SHIMADZU UV-1900 spectrophotometer (Shimadzu Scientific Instruments, Inc., Columbia, MD, USA).

2.6. Statistical Analysis

All samples for the experiments were prepared in triplicate; control samples, in duplicate. The results are represented as mean ± standard deviation. A one-way analysis of variance (ANOVA) and a Tukey–Kramer multiple comparison test (p = 0.05) were performed to determine whether the differences in the TAN and Fe removal for different leachates and dilutions were significant. Tests of correlation were performed to understand the correlation between the removal rate of the pollutant and its initial concentration, and biomass concentration. All statistical analyses were performed with the function Data Analysis Tools in Excel 2016. The results of the statistical analyses are provided in Supplementary Materials, Tables S1–S4.

3. Results and Discussion

3.1. The Initial Ecotoxicity Test

The results obtained from the initial ecotoxicity test are displayed in Figure 1 (D. subspicatus), Figure 2 (S. obliquus), and Figure 3 (C. vulgaris). For C. vulgaris, the test was completed after 11 days of culturing, as long as no cell growth in sample A was observed during this period. Sample D appeared to be more prosperous for C. vulgaris growth, since stable exponential cell growth was observed from the 6th day of culturing. Nevertheless, the severe overall growth inhibition was obvious when compared with the control. The cultivation of S. obliquus and D. subspicatus lasted for 21 and 24 days, respectively. During these periods, the change in color of the samples from light brown to deep green was observed. The highest increase in cell concentration was reached for D. subspicatus in both the A and D samples; therefore, D. subspicatus was selected for further experiments.

3.2. Effect of Landfill Leachates on Algae Growth.

For microalgae growth, medium composition is essential. One of the key factors for microalgae cultivation is an optimal N:P ratio. According to the literature, this relationship varies depending on the microalgal strain [41], and the proper N:P ratio for freshwater microalgae growth ranges between 6.8 and 13.0 [42,43]. As previously mentioned, LL compositions are generally characterized by very high concentrations of TAN and TOC [7,44], which may affect the nutrient balance. The data in Table 2 reveal that the N:P ratios in most of the tested LLs were several times higher than the optimum reported in the literature. This imbalance in particular was exceedingly strong in samples B and E. As shown in Figure 4b,e, no biomass growth was observed in those samples, and the total growth inhibition occurred for all ratios after 8 days of cultivation.
On the contrary, in sample F, the N:P ratio was critically low (2:1), which might also reflect the slow culture adaptation and growth. To avoid such a problem in further studies, N:P ratio balancing is recommended. For example, the appropriate N:P ratio could be achieved by integrated treatment through mixing LL and some type of WW with a higher phosphorus content [45].
Another limiting factor for algae growth in the LL could be insufficient light penetration due to the high concentration of dissolved compounds. Depending upon both landfill composition and age, leachate contains different organic fractions. In mature landfills, humic and fulvic acids comprise over 60% of the organic matter [46]. These compounds are characterized by their intense dark color. The absolute maximum TOC concentration was measured in sample F (6919 mg·L−1). Such a high TOC content is in accordance with the fact that organic solid waste comprised the predominant fraction of all disposed waste of the landfill. Our results show a strong correlation between TOC content and the leachate’s color expressed via absorbance at 680 nm (r = 1) and 455 nm (r = 0.89). The LLs are depicted in Figure 5.
The pH range was also taken into account as one of the major growth factors. The LL pH values were found to be in accordance with the optimal conditions reported in the literature [28]; hence, pH adjustment was not required (Table 2). The best culturing results were achieved for samples A (10%, 20%, 50%), C (10%, 20%), and D (10%, 20%). As can be seen from the growth curves (Figure 4a), the adaptation of the algal culture in sample A was very fast and the immediate exponential growth of the culture was observed for 10% and 20% LL dilutions. For sample A (50%), the lag phase lasted 4 days and was followed by rapid biomass growth in the exponential phase. Overall, the growth curves of sample A were comparable with the control (Figure 4a). Sample D also showed promising results for 10% and 20% dilutions. In both dilutions, no lag phase was observed. Similarly to sample A, the culture exhibited an immediate exponential growth that continued for approximately 6 days. In terms of sample C, the cultivating results are comparable to those of sample D.
As shown in Figure 4c,d, for 10% and 20% dilutions, a very short lag phase (about 2 days) was followed by rapid exponential algae growth continuing for two weeks. It is also notable that, after a short-term lysis phase, for sample C (20%), a second exponential growth phase was observed; and that when compared to the control sample, the overall growth progress of sample C (20%) was practically identical, which testifies to the highly favorable growing conditions of the sample.
As mentioned previously, light intensity plays one of the key roles in the cultivation of photosynthetic microorganisms. For sample F, no biomass growth was observed, most likely as a consequence of its deep black color (Figure 4f). The initial optical density (OD) of this sample was very high (up to 3.7), which was taken into account for biomass growth evaluation.
The overall results of microalgae culturing suggest that LL can be an applicable medium for microalgae growth. However, no amendments of leachate parameters were made at this stage. For LL composition optimization and biomass yield increase, the adjustment of the particular LL parameters—above all, the N:P ratio—should be considered for further studies.

3.3. Pollutant Removal

3.3.1. TAN and Fe Removal Efficiency of D. subspicatus

The results of TAN removal are shown in Figure 6. The overall TAN remediation efficiency of D. subspicatus ranged between 14.2% and 73.4%, and the highest removal rate was reached in samples A and D. The overall results obtained for TAN removal are comparable with those reported in the literature. Pereira et al. [47] evaluated the potential of C. vulgaris for biomass production and nutrient removal from LL of various compositions. The authors reported an effective removal of TAN (36–100%); however, microalgal growth was higher in the leachate with the lowest TAN concentration. These results correlate with those achieved by Park et al. [48]. In their study, the effect of TAN and Fe concentration in mixed wastewater on biomass production and biochemical content of microalgae was investigated. The results of the study showed that TAN had a strong effect on microalgal growth, as the microalgal growth curve under ammonium was below the control. Another study focused on evaluating the effect of different artificial wastewaters on cultivation of Desmodesmus sp. The obtained results demonstrated that green algae Desmodesmus can adapt well in wastewater, and the removal of 83.78–100% of initial TAN was achieved [49].
To evaluate the Fe-remediating potential of D. subspicatus, the initial and output concentrations of Fe were determined, and the calculated removal efficiency was expressed in percentage points. The correlation between biomass concentration and Fe removal was also determined by means of a correlation test. Removal rates are displayed in Figure 7.
We found Fe to be removed very effectively in the leachates A, C, and D (10%) and D (20%), in which Fe content was completely exhausted after remediation. In leachates B, E, and F, the removal efficiency was significantly lower, which was most likely caused by the premature growth inhibition of the microalgae in the samples. These results correspond with those of the statistical tests, which showed a relatively strong correlation between biomass concentration and Fe remediation efficiency (r = 0.81). Similarly, the correlation analysis showed the direct correlation of TAN removal efficiency with the biomass concentration (r = 0.79) of the samples.
A 100% removal of Fe content in sample C (50%) may seem to conflict with the statistical analysis; nevertheless, this result most likely owes to the low input concentration of Fe in this sample (0.36 mg∙L−1), which allowed its complete exhaustion despite the low cell concentration.
It is most notable that we could observe Fe removal in samples B, E, and F, despite their critically low biomass concentration and high Fe input load. For example, the average removal rate reached 22.7% in sample B and 17.4% in sample F (the input Fe concentrations of non-diluted leachates were 5.1 and 42.0 mg·L−1, respectively). Due to high leachate toxicity, a strong growth inhibition in samples B, E, and F was observed, and maximum cell concentrations were significantly lower than those of leachates A, C, and D (Figure 7). Presumably, biosorption by dead biomass was the key mechanism of metal removal in leachates B, E and F, rather than bioaccumulation. It is known that non-living algal biomass has an ability to bind metals to cell surfaces, which is widely used in biological methods of heavy metal elimination. The absorption mechanisms of living or non-living microalgae are different, and the amount of removed contaminants may vary among the species [50]. For example, Abla et al. [51] investigated the uptake of iron and lead from aqueous solution by C. vulgaris. It is most notable that the uptake percentage significantly increased for non-living cells compared to living cells, from 51.51% to 79.03% for Fe2+, and from 0.12% to 35.28% for Pb2+, respectively. Several authors have also investigated the mechanism of biosorption with respect to Cr6+ removal from contaminated water. Sutkowy and Klosowski [52] achieved 70% removal of Cr6+ ions with an initial chromium concentration of 10 mg·L−1 and a biomass concentration of 2 g·L−1. These results coincide with those reported by Sibi [53] who revealed that the biosorption efficiency of Cr6+ was significantly affected by the dose of the biosorbent, and approximately 60% removal of Cr6+ was achieved with a biomass concentration of 2.5 g·L−1. Higher dosages of biomass did not affect the efficiency significantly. This suggests that metal removal by microalgae biomass may occur even in a highly unfavorable medium by means of a passive biosorption mechanism.
To assess the effect of the dilutions and initial parameters of LL on pollutant removal, a correlation analysis was conducted using the correlation matrices with four variables (PAR 1–4). The variables were: remaining TAN concentration (PAR 1); remaining Fe concentration (PAR 2); initial TAN concentration (PAR 3); and initial Fe concentration (PAR 4).
The statistical analysis revealed linear correlations between the same variables for each dilution: PAR 1–PAR 3 and PAR 2–PAR 4. The results indicate a strong positive correlation between the initial and remaining concentration of Fe, irrespective of dilutions (r = 0.984, 0.989, and 0.999 for 10%, 20%, and 50%, respectively). Similarly, a positive linear correlation between the initial and remaining TAN was revealed for all dilutions (r = 0.994, 0.995, and 0.990 for 10%, 20%, and 50%, respectively). In other words, independent of dilutions, the remediation efficiency of Fe and TAN was conditioned by the initial content of Fe and TAN, respectively.
Based on the results of the ANOVA test, the removal of TAN and Fe significantly differed between the leachates (p < 0.05). In other words, the remediation efficiency of the particular leachate was significantly affected by its input parameters and overall profile composition. The results of the ANOVA test also indicated that the Fe removal efficiency of the particular leachate was not significantly affected by its dilution (p < 0.05). According to the results of the Tukey–Kramer test, the dilutions were important only for leachate D. In particular, 50% dilution was significantly different from the others. Overall, the statistical analyses showed that the role of dilution was negligible for Fe removal.
As for TAN removal, the effect of dilution on the removal rate was observed for leachates C, D and F. The results of subsequent Tukey–Kramer multiple comparison revealed that for leachates C and D, only 50% dilution was significantly different. Solely for leachate F were all three dilutions important. As for TAN, dilution had a higher impact on its remediation efficiency. However, the results indicate that only a difference between 20% and 50% dilutions was significant. This yields the conclusion that for the remediation efficiency which was achieved in the present study, 1:5 and 1:2 dilution ratios of the leachates were sufficient for TAN and Fe removal, respectively. Moreover, the results suggest that the overall remediation efficiency was dependent on the general LL composition rather than just the dilution ratio, which coincides with the conclusions of Paskuliakova et al. [39] in the microalgal bioremediation of nitrogenous compounds of LL.

3.3.2. Possible Optimizations of the Phycoremediation Process

In LL treatment, microalgal culture is exposed to highly toxic conditions, which are in some cases incompatible with algal photosynthetic activity and overall cell growth. The most important factors contributing to LL toxicity are high turbidity, N:P ratio imbalance, high content of ammonia nitrogen, and high concentration of metals. To improve the conditions for algae to thrive in LL and to make LL more favorable for biomass cultivation, various techniques are applied. For example, Quan et al. [54] examined LL pre-treatment by ozonisation. According to the results, macromolecular organics were reduced from 93.3% in the untreated LL to 54.8% in the oxidized LL, which in turn enhanced microalgal growth and nitrogen removal. In another study [55], the authors performed LL concentrate remediation by applying microalgae assisted with electrooxidation. Using this approach, a significant decline in TOC and TAN content was achieved. However, the authors noted that these oxidative techniques have at best a negligible effect on the removal of inorganic salts.
To reduce non-oxidative pollutants and to mitigate LL toxicity to boost microalgae culturing, LL dilution by water appears to be an appropriate approach. However, in some cases, the amount of water required would make the process unsustainable and barely possible. According to the review by Nawaz et al. [56], freshwater consumption is one of the major drawbacks of the LL phycoremediation process as it increases material costs and water footprint. To address this issue, several studies have investigated the basic idea of utilizing an LL–wastewater mixture as the medium for microalgal growth. For example, Zhao et al. [57] cultivated a microalgae–bacteria consortium with Chlorella pyrenoidosa in municipal wastewater spiked with up to 20% of LL. The authors reported that up to 90% of nitrogen was removed in a culture with 10% LL spike ratio with a pre-treatment nitrogen concentration of 221.6 mg∙L−1. Hernandez-Garcia et al. [45] used a microalgae–bacteria consortium with Desmodesmus spp. and S. obliquus to treat a mixture of raw municipal wastewater with different leachate ratios (0%, 7%, 10%, and 15%) for biomass, carbohydrate and lipid production. This treatment achieved a significant decrease in the TAN and orthophosphate concentrations (82% and 43%, respectively) from a wastewater–leachate mixture initially containing 167 mg∙L−1 of TAN and 23 mg∙L−1 of orthophosphate. Moreover, Desmodesmus spp. was observed to accumulate high lipid (20%) and carbohydrate (41%) content. Nawaz et al. [56] also suggested that phosphorus deficiency typical to LL may be remedied by diluting the LL with agricultural effluent rich in phosphorus.
Recently, a number of studies have been dedicated to wastewater remediation via passive biosorption mechanism by dead algal biomass. This approach proved to effectively eliminate contaminants such as heavy metals [51,52,53] and pharmaceuticals [58,59]. However, it follows from the literature that for biosorption, a relatively high biomass concentration is required, since non-living biomass is incapable of cell reproducing, and its concentration is fixed. Especially in the case of high initial contamination load of the medium, it may reflect increased remediation costs. On the other hand, the use of non-living biomass may address the major drawbacks of phycoremediation—above all, fresh water demand for medium dilution. Moreover, dried biomass is not sensitive to medium toxicity, which enables it to be utilized for the remediation of toxic aquatic run-offs. Hence, it appears that the effectiveness of using non-living or living biomass should be assessed based on the composition of the particular wastewater to treat.
The use of such approaches would be useful in large-scale implementations to reduce freshwater demand and minimize the environmental footprint of the phycoremediation process.

4. Conclusions

We successfully demonstrated that phycoremediation has the potential to be a reliable pre-RO step for LL treatment. The green microalgal D. subspicatus exhibited a high ability to remove TAN and iron from LL, thereby indicating its potential to prevent premature RO-membrane clogging and reduce NH4+–N loss from the LL concentrate. Notably, the removal efficiency of TAN was relatively high even in the extremely toxic sample. Our results thus show that D. subspicatus is capable of tolerating media with high concentrations of TAN, which is essential for the application of phycoremediation in LL treatment. Moreover, we demonstrated that independent of dilution, the remediation efficiency of Fe and TAN was conditioned by the initial content of Fe and TAN, respectively. The overall results suggest that the remediation efficiency was dependent on the general LL composition rather than just the dilution ratio. Although the remediation did not appear to be equally efficient for all samples, our study suggests that the remediation efficiency can be significantly increased by optimizing the LL input parameters. To provide a further investigation of LL pre-treatment by phycoremediation, our next step will be to design a function model that combines an RO-membrane unit and a batch bioreactor for simulating the treatment process.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4441/12/6/1755/s1, Table S1: Correlation matrices for the assessment of correlation between the initial parameters and dilutions of the LL and pollutant removal. The tested means: remaining TAN concentration (PAR 1); remaining Fe concentration (PAR 2); initial TAN concentration (PAR 3); and initial Fe concentration (PAR 4). For each pair the r−value was determined, Table S2: Test of correlations between the biomass concentration and Fe/TAN removal efficiency, Table S3: One−way analysis of variance for determination whether group mean differences exist in the values of remediation efficiency between the particular leachates and dilutions (p = 0.05), Table S4: Tukey—Kramer multiple comparison test (p = 0.05), Table S5: BBM medium composition.

Author Contributions

Conceptualization, R.V., M.Š., and L.T.; methodology, R.V.; validation, M.Š.; formal analysis, L.T.; investigation, M.K. and M.M.; resources, M.Š. and V.K.; data curation, M.Š. and R.V.; writing—original draft preparation, M.K.; writing—review and editing, V.K.; visualization, L.T. and M.M.; supervision, V.K.; project administration, R.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by specific university research MSMT No 21-SVV/2019.

Acknowledgments

The authors wish to thank the operators of the landfills in Ghana and Czechia for the supplied samples. The authors also wish to express special gratitude to Jakub Hadámek and José Ramos III for the valuable advice.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Water 12 01755 g001 550
Figure 1. The initial ecotoxicity test for Desmodesmus subspicatus in leachates A (Czechia) and D (Ghana). Both leachates were diluted with distilled water to a concentration of 20%. The error bars indicate standard deviations.
Figure 1. The initial ecotoxicity test for Desmodesmus subspicatus in leachates A (Czechia) and D (Ghana). Both leachates were diluted with distilled water to a concentration of 20%. The error bars indicate standard deviations.
Water 12 01755 g001
Water 12 01755 g002 550
Figure 2. The initial ecotoxicity test for Scenedesmus obliquus in leachates A (Czechia) and D (Ghana). Both leachates were diluted with distilled water to a concentration of 20%. The error bars indicate standard deviations.
Figure 2. The initial ecotoxicity test for Scenedesmus obliquus in leachates A (Czechia) and D (Ghana). Both leachates were diluted with distilled water to a concentration of 20%. The error bars indicate standard deviations.
Water 12 01755 g002
Water 12 01755 g003 550
Figure 3. The initial ecotoxicity test for Chlorella vulgaris in leachates A (Czechia) and D (Ghana). Both leachates were diluted with distilled water to a concentration of 20%. The error bars indicate standard deviations.
Figure 3. The initial ecotoxicity test for Chlorella vulgaris in leachates A (Czechia) and D (Ghana). Both leachates were diluted with distilled water to a concentration of 20%. The error bars indicate standard deviations.
Water 12 01755 g003
Water 12 01755 g004a 550Water 12 01755 g004b 550Water 12 01755 g004c 550
Figure 4. The growth curves of D. subspicatus in samples: (a) leachate A; (b) leachate B; (c) leachate C; (d) leachate D; (e) leachate E; and (f) leachate F. Each chart represents the growth curves of D. subspicatus in three dilutions (10%, 20%, 50%) of the particular landfill leachate, along with the control. The error bars indicate standard deviation.
Figure 4. The growth curves of D. subspicatus in samples: (a) leachate A; (b) leachate B; (c) leachate C; (d) leachate D; (e) leachate E; and (f) leachate F. Each chart represents the growth curves of D. subspicatus in three dilutions (10%, 20%, 50%) of the particular landfill leachate, along with the control. The error bars indicate standard deviation.
Water 12 01755 g004aWater 12 01755 g004bWater 12 01755 g004c
Water 12 01755 g005 550
Figure 5. Centrifuged and filtered landfill leachate samples (non-diluted) before treatment. The abbreviations A, B, C, D, E, and F correspond to the leachates A, B, C, D, E, and F, respectively. The leachate colors expressed via absorbance at 455 and 680 nm had a strong direct correlation with the TOC load of the leachates (for details see Table 2).
Figure 5. Centrifuged and filtered landfill leachate samples (non-diluted) before treatment. The abbreviations A, B, C, D, E, and F correspond to the leachates A, B, C, D, E, and F, respectively. The leachate colors expressed via absorbance at 455 and 680 nm had a strong direct correlation with the TOC load of the leachates (for details see Table 2).
Water 12 01755 g005
Water 12 01755 g006 550
Figure 6. Total ammonia nitrogen (TAN) removal efficiency of D. subspicatus during the landfill lechate treatment. The columns indicate percentage removal of TAN (total ammonia nitrogen) in the samples. The bars indicate maximum biomass concentration of D. subspicatus in the samples. The error bars indicate standard deviation.
Figure 6. Total ammonia nitrogen (TAN) removal efficiency of D. subspicatus during the landfill lechate treatment. The columns indicate percentage removal of TAN (total ammonia nitrogen) in the samples. The bars indicate maximum biomass concentration of D. subspicatus in the samples. The error bars indicate standard deviation.
Water 12 01755 g006
Water 12 01755 g007 550
Figure 7. Fe removal efficiency of D. subspicatus during the landfill leachate treatment. The columns indicate Fe removal in the samples. The bars indicate maximum biomass concentration of D. subspicatus in the samples. The error bars indicate standard deviation.
Figure 7. Fe removal efficiency of D. subspicatus during the landfill leachate treatment. The columns indicate Fe removal in the samples. The bars indicate maximum biomass concentration of D. subspicatus in the samples. The error bars indicate standard deviation.
Water 12 01755 g007
Table
Table 1. Characteristics of the landfills from which the samples originated.
Table 1. Characteristics of the landfills from which the samples originated.
LandfillLocationOperation Period (Year)Reported Annual Waste Disposal (Metric Tons)
ALiberec Region, Czechia2007–present29,000
BCentral Bohemian Region, Czechia1993–present80,000
CÚstí nad Labem Region, Czechia2004–present100,000–140,000
DGa South Municipal Assembly, Accra, Ghana2002–present396,000
EGa South Municipal Assembly, Accra, Ghanafirst phase: 1991–2001;
second phase: 2008–2010		438,000
FKpone-Katamanso District Assembly, Kpone, Ghana2012–present295,000
Table
Table 2. Characteristics of the non-diluted landfill leachates (LLs) before treatment.
Table 2. Characteristics of the non-diluted landfill leachates (LLs) before treatment.
ParametersABCDEF
pH8.08.08.27.88.39.5
Electrical conductivity, μS·cm−112,50018,80010,70018,70019,70019,500
Ammonia nitrogen, mg·L−13803584387611315634
Nitrate, mg·L−1224244713364.838.9<0.05
Phosphorus, mg·L−13.87.44.15.512.314.6
N:P95.7473.585.194.1212.01.9
Total organic carbon, mg·L−12486332393489856919
Total inorganic carbon, mg·L−1614148079644923262931
Total carbon, mg·L−18622114103579633129850
Absorbance at 680 nm0.0540.1420.0510.1410.3982.884
Absorbance at 455 nm0.4340.9500.3790.8172.5024.000
Fe, mg·L−10.675.100.726.6012.4042.00

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Kholomyeva, M.; Vurm, R.; Tajnaiová, L.; Šír, M.; Maslova, M.; Kočí, V. Phycoremediation of Landfill Leachate with Desmodesmus subspicatus: A Pre-Treatment for Reverse Osmosis. Water 2020, 12, 1755. https://doi.org/10.3390/w12061755

AMA Style

Kholomyeva M, Vurm R, Tajnaiová L, Šír M, Maslova M, Kočí V. Phycoremediation of Landfill Leachate with Desmodesmus subspicatus: A Pre-Treatment for Reverse Osmosis. Water. 2020; 12(6):1755. https://doi.org/10.3390/w12061755

Chicago/Turabian Style

Kholomyeva, Marina, Radek Vurm, Lucia Tajnaiová, Marek Šír, Mariya Maslova, and Vladimír Kočí. 2020. "Phycoremediation of Landfill Leachate with Desmodesmus subspicatus: A Pre-Treatment for Reverse Osmosis" Water 12, no. 6: 1755. https://doi.org/10.3390/w12061755

APA Style

Kholomyeva, M., Vurm, R., Tajnaiová, L., Šír, M., Maslova, M., & Kočí, V. (2020). Phycoremediation of Landfill Leachate with Desmodesmus subspicatus: A Pre-Treatment for Reverse Osmosis. Water, 12(6), 1755. https://doi.org/10.3390/w12061755

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