Removal of water pollutants by Lemna gibba

Greenhouse gases emissions from duckweed pond system treating polyester resin wastewater containing 1,4-dioxane and heavy metals

Phytoremediation of polyester resin wastewater containing 1,4-dioxane and heavy metals using Lemna gibba (L.gibba) was enhanced by incorporation of perforated polyethylene carrier materials (PCM) onto the duckweed pond (DWP) system. The DWP module was operated at a hydraulic retention times (HRTs) of 2, 4 and 6 days and as well as 1,4-dioxane loading rate of 16, 25 and 48 g/m³.d. The maximum removal efficiency of 54 ± 2.5% was achieved for 1,4-dioxane at an HRT of 6 days and loading rate of 16 g1,4-dioxane/m³.d. Similarly, the DWP system provided removal efficiencies of 28.3 ± 2.1, 93.2 ± 7.6, 95.7 ± 8.9 and 93.6 ± 4.9% for Cd²⁺, Cu²⁺, Zn²⁺ and Ni²⁺ at influent concentration of 0.037 ± 0.01, 1.2 ± 0.9, 27.2 ± 4.7 and 4.6 ± 1.2 mg/L respectively. The structural analysis by Fourier-transform infrared spectroscopy (FTIR) clearly displayed a reduction of 1,4- dioxane in the treated effluent. A strong peak was detected for L. gibba plants at frequency of 3417.71 cm^-1 due to N-H stretching, which confirm the proposed mechanism of partially conversion of 1,4-dioxane into amino acids. Glycine, serine, aspartic, threonine and alanine content were increased in L. gibba by values of 35 ± 2.2, 40 ± 3.2, 48 ± 3.7, 31 ± 2.8, and 56 ± 4.1%, respectively. The contribution of DWP unit as a greenhouse gases (GHG) emissions were relatively low (1.65 gCO₂/Kg BOD_removed.d., and 18.3 gCO₂/Kg _biomass.d) due to photosynthesis process, low excess sludge production and consumption of CO₂ for nitrification process (1.4 gCO₂/kgN _removed.d). Based on these results, it is recommended to apply such a technology for treatment of polyester resin wastewater containing 1,4-dioxane and heavy metals at a HRT not exceeding 6 days.

Phytoremediation potential of the duckweeds Lemna minuta and Lemna minor to remove nutrients from treated waters

Phytoremediation potential of duckweeds (Lemna minuta, Lemna minor) to remove nutrients from simulated wastewater was analyzed. In two separate experiments, the two species were grown for 28 days in waters enriched with nitrate and phosphate to simulate nutrient concentrations of domestic wastewater. Water physical and chemical measurements (temperature, pH, conductivity, oxygen) and plant physiological and biochemical analysis (biomass, relative growth rate-RGR, nutrient and chlorophyll contents, peroxidative damage, bioconcentration factor-BCF) were made to test and compare the phytoremediation capacity of the two Lemna species. L. minuta biomass increased almost tenfold during the time-course of the treatment resulting in a doubling of the mat thickness and a RGR of 0.083 ± 0.001 g/g day. Maximum frond content of phosphate was reached by day 21 (increase over 165%) and nitrate by day 7 (10%). According to the BCF results (BCF > 1000), L. minuta was a hyperaccumulator for both nutrients. On the other hand, L. minor biomass and mat thickness decreased continuously during incubation (RGR = - 0.039 ± 0.004 g/g day). In L. minor fronds, phosphate content increased until day 14, after which there was a decrease until the end of the incubation. Frond nitrate content significantly decreased by day 7, but then remained relatively constant until the end of the experiment. L. minor proved to be hyperaccumulator for phosphates, but not for nitrates. Results indicated L. minuta has a greater potential than L. minor to remove both nutrients by bioaccumulation, especially phosphates, demonstrated also by better physiological and biochemical responses. However, during the incubation, the chlorophyll content of L. minuta mat did continuously decrease and peroxidative damage had increased until day 14, indicating that the system was under some kind of stress. Strategies to avoid this stress were discussed.

An attempt to electrically enhance phytoremediation of arsenic contaminated water

Water polluted with arsenic presents a challenge for remediation. A combination of phyto- and electro-remediation was attempted in this study. Four tanks were setup in order to assess the arsenic removal ability of the two methods separately and in combination. Lemna minor was chosen for As remediation and collected from a ditch in Utrecht, The Netherlands. The tanks were filled with surface water without any pre-cleaning, therefore containing various elements including metals as Mn (2.9 mg L(-1)), Cu (0.05 mg L(-1)), Fe (1.39 mg L(-1)), and Ba (0.13 mg L(-1)). This water was then spiked with As and allocated to a feed container, guaranteeing a continuous flow of 0.12 mL s(-1) to each tank. Two experiments were performed: Exp. 1 with 3 consecutive stages with rising applied voltage and Exp. 2, with a constant voltage over a period of 6 d. Measurements of pH and temperature were taken every working day, as well as water samples from outlets of all tanks including feed container for control. From the present study, there was no evidence that As had been taken up by the plants, but a strong depletion of As was observed in the tanks where current was applied. Preliminary results clearly showed that applying voltage to the electrodes caused 90% removal of As from the spiked surface water.

Comprehensive modeling of mat density effect on duckweed (Lemna minor) growth under controlled eutrophication

The effect of mat density on duckweed (Lemna minor) growth was studied under controlled conditions: 12.5h a day light exposure and 342 mol m(-2) s(-1) light intensity at 20 degrees C. The plant growth was carried out in Hoagland medium for 7 days without harvesting. The results revealed a maximal biomass growth rate of 88 g-dry m(-2) (1,470 g-wet m(-2)) at an optimal initial mat density of 45 g-dry m(-2) (750 g-wet m(-2)), with removal rates for nitrogen (N) and phosphorus (P) of 483 mg-Nm(-2) d(-1) and 128 mg-Pm(-2) d(-1), respectively. A mathematical model that takes into account the mat density was developed in order to simulate the growth of Lemna minor under controlled eutrophication. Based on experiments carried out, the model exhibits a reliability of 89% . The model remains to be validated at the full-scale level.