USING DECOMPOSITION AND RESPIRATION RATES TO EVALUATE THE DUAL USE OF PLANTS FOR PHYTOREMEDIATION AND CARBON SEQUESTRATION

Mining creates substantial amounts of waste which can pollute the environment with heavy metals such as lead and copper. Certain plant species can hyperaccumulate these heavy metals and be used to remediate contaminated sites. This study compares the hyperaccumulation potential of five plant species, Tithonia rotundifolia, Typha domingensis, Cymbopogon citratus, Pennisetum setaceum and Lantana camara, in soils contaminated with lead and copper. By assessing the hyperaccumulation potential of these plants, the study will determine which plants are more effective in phytoremediation of lead and copper-contaminated soils. In addition, the study evaluates the ability of these plants to sequester carbon, which can help mitigate climate change by reducing atmospheric carbon dioxide (CO₂) levels. Soil and plant samples were collected from the field in contaminated sites in Zambian mining towns. Using X-Ray Fluorescence, heavy metal concentrations were measured in the samples. Decomposition rates were indirectly measured using in-situ CO₂ chambers and directly in the lab using decomposition bottles and CO₂ sensors. These rates were used to compare carbon sequestration potential of the plants. Preliminary results showed Tithonia rotundifolia to have the greatest rates of decomposition while Typha domingensis had the lowest rates both in the field and in the lab. Tithonia rotundifolia leaves were found to have the highest amount of lead and copper accumulation while Lantana camara was second. These results suggest that Typha domingensis would be a good phytoremediation plant for carbon sequestration and combating climate change when compared to the other plants in this study. Through careful analysis and experimentation, this research will provide insights into the potential use of these plants for both heavy metal remediation and carbon sequestration in contaminated soils. This material is based upon work supported by the National Science Foundation under Grant No. 2107177.