Mapping the Bioremediation Potential of Post-Mining Land Contaminated with Heavy Metals (Cd and Pb) Based on Local Vegetation Distribution Data and Soil Organic Matter Content Statistics
DOI:
https://doi.org/10.70076/cj.v2i1.143Keywords:
Bioremediation, Heavy Metals, Post-Mining Land, Soil Organic Matter, Phytoremediation, Indigenous Species, IndonesiaAbstract
This study aims to map the bioremediation potential of post-mining land contaminated with Cd and Pb by integrating soil organic matter statistics and indigenous vegetation distribution data. The post-extraction landscapes across the Indonesian archipelago are currently facing a dual crisis of severe pedological degradation and hazardous lithogenic contamination, specifically involving Cadmium (Cd) and Lead (Pb). This research presents a comprehensive geospatial and statistical mapping of bioremediation potential by synthesizing secondary data streams from the Ministry of Environment and Forestry (KLHK), the Ministry of Energy and Mineral Resources (ESDM), and the Indonesian Soil Research Institute (2020–2025). The study evaluates the synergy between Soil Organic Matter (SOM) concentrations and the adaptive distribution of indigenous vegetation. Quantitative analysis reveals that SOM acts as a critical biogeochemical regulator; a robust positive correlation exists between SOM levels and the immobilization efficiency of toxic divalent cations. Through the application of Bio-Concentration Factors () and Translocation Indices (), the study identifies specific local cohorts, such as Pteris vittata, Cyperus rotundus, and Ipomea asarifolia, as high-priority hyperaccumulators capable of thriving in substrates with SOM levels as low as 0.85%. The findings establish that successful land rehabilitation requires an integrated approach: pedological priming to elevate SOM, followed by targeted phytoremediation using regional botanical assets. This research provides an evidence-based framework for national reclamation policies, shifting the paradigm from mere superficial revegetation to functional, data-driven ecological restoration.
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