Conference Publication Details
Mandatory Fields
A. Paskuliakova, Steve Tonry, Nicolas Touzet
Microalgae isolation and selection for the treatment of landfill leachate
In Press
Optional Fields
phycoremediation, landfill leachate, microalgae, nutrient limitation
The use of microalgae in remediation has been researched for a variety of waste effluents, yet algal remediation of landfill leachate is somewhat less explored. Very high levels of pollutants, such as ammonia nitrogen, salts and recalcitrant organic matter are present in landfill leachate and render it toxic to many organisms. Thus the selection of suitably tolerant microalgal strains is crucial for phycoremediation attempts. Other factors such as temperature and light requirements and the variable composition of landfill leachates also need to be incorporated into the remediation strategy. This study focused on isolating microalgae strains from different environments in the North-West of Ireland, which might have the potential to use leachate pollutants as their source of nutrients. A screening process was applied to select the most promising strains which was followed by a preliminary assessment of nutrient depletion. Altogether 34 strains were obtained from marine, freshwater and polluted environments. Further screening yielded 16 strains capable of growth in leachate samples to varying degrees. Generally, the strains isolated from landfill leachate itself appeared to perform better, while some freshwater and marine species could adapt if the leachate was appropriately diluted. A preliminary nutrient depletion experiment with the chlorophyte strain Chlamydomonas sp. SW13aLS grown on 10% permeate leachate indicated a substantial reduction in nutrients such as ammonia-nitrogen (93%) and nitrate (54%) when supplemented with phosphorus. The results demonstrate the possible application of microalgae for the treatment of leachate when grown under limited light and relatively low temperature; however nutrient limitation could be a key inhibitory factor requiring optimisation.
Grant Details