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The repurposing of gold (Au) mine tailings from artisanal and small-scale mining (ASGM) operations via alkali activation technology is a promising strategy for waste reduction in developing countries. Direct activation of mine tailings, however, is challenging because these materials contain relatively low aluminum (Al)-bearing minerals. In this study, palm oil fuel ash (POFA) was elucidated as a high Al-bearing waste derived-admixture for the synthesis of an ASGM tailings-based geopolymer composite. Semi-quantitative XRD analysis showed that the tailings contained quartz (SiO2) (~58%), pyrite (FeS2) (~20%) and calcite (CaCO3) (~15%) with minor to trace amounts of aluminosilicates (~7%). Substantial amounts of environmentally regulated pollutants such as mercury (Hg) (40 mg/kg), lead (Pb) (8430 mg/kg) and arsenic (As) (300 mg/kg) were also found in the tailings. SEM-EDS, XRD and ATR-FTIR results showed the successful formation of a hybrid geopolymer-CASH matrix, which improved the unconfined compressive strength (UCS) of geopolymer composites from ~5 MPa to ~7 MPa. Furthermore, POFA did not significantly affect the thermal resistivity of geopolymer composites based on thermal analysis. Finally, the TCLP results showed that the Pb leaching concentrations from ASGM tailings exceeded environmental standards (~15,000 µg/L), which was suppressed after alkali activation to 300–500 µg/L. This means that POFA addition to ASGM tailings-based geopolymer composite improved not only its applicability as backfill, pavements and bricks but also its ability to immobilize toxic elements.

In this study, chemical and mineralogical characterizations of gold-mine tailings in key mining areas in Mindanao, Philippines were investigated for possible utilization as geopolymeric source material. Results of X-ray fluorescence (XRF) and energy dispersive X-ray spectroscopy (EDS) showed that the mine tailings samples have significant amounts of silicon, aluminum and calcium, which are crucial elements needed for geopolymerization. This was confirmed by the IR spectroscopic and mineralogical characteristics of the tailings where vibration bonds and minerals associated with Al and Si such as kaolinite and zeolite are detected. These minerals are already established as indicators for a material to be a good feedstock for geopolymerization. Furthermore, one of the tailings samples had an Si/Al ratio of 4.81, which was close to the recommended value of 3.0 for geopolymerization. The compressive strength of the synthesized geopolymer bricks gained an average of 5.48 MPa. The results suggested that gold mine tailings from key mining areas in Mindanao, Philippines could be used as geopolymer source material.

Microbially induced carbonate precipitation (MICP), a widespread phenomenon in nature, is gaining attention as a low-carbon alternative to ordinary Portland cement (OPC) in geotechnical engineering and the construction industry for sustainable development. In the Philippines, however, very few works have been conducted to isolate and identify indigenous, urease-producing (ureolytic) bacteria suitable for MICP. In this study, we isolated seven, ureolytic and potentially useful bacteria for MICP from marine sediments in Iligan City. DNA barcoding using 16s rDNA identified six of them as Pseudomonas stutzeri, Pseudomonas pseudoalcaligenes, Bacillus paralicheniformis, Bacillus altitudinis, Bacillus aryabhattai, and Stutzerimonas stutzeri but the seventh was not identified since it was a bacterial consortium. Bio-cementation assay experiments showed negligible precipitation in the control (without bacteria) at pH 7, 8, and 9. However, precipitates were formed in all seven bacterial isolates, especially between pH 7 and 8 (0.7–4 g). Among the six identified bacterial species, more extensive precipitation (2.3–4 g) and higher final pH were observed in S. stutzeri, and B. aryabhattai, which indicate better urease production and decomposition, higher CO2 generation, and more favorable CaCO3 formation. Characterization of the precipitates by scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) and attenuated total reflectance Fourier transform spectroscopy (ATR-FTIR) confirmed the formation of three carbonate minerals: calcite, aragonite, and vaterite. Based on these results, all six identified indigenous, ureolytic bacterial species from Iligan City are suitable for MICP provided that the pH is controlled between 7 and 8. To the best of our knowledge, this is the first report of the urease-producing ability and potential for MICP of P. stutzeri, P. pseudoalcaligenes, S. stutzeri, and B. aryabhattai.

Landslides along highways in Mindanao including the Cagayan de Oro (CDO)-Bukidnon-Davao City (Buda) highway pose high risk to many motorists and passengers. It also hampers the flow of transportation affecting the economy of the region not counting the cost of road maintenance and rehabilitation. This study aimed to evaluate the casual factors of rainfall-induced landslide along the study area using Geographic Information System (GIS) and Frequency Ratio (FR) approach. Global Positioning System (GPS) was used to determine the coordinates of 78 landslide locations recorded. Map layers of different parameters with FR values were overlaid to produce the rainfall-induced Landslide Susceptibility Index (LSI) map. The generated LSI map was classified into low, moderately low, moderate, moderately high and high risk. Results showed that rainfall coupled with soil type and slopes were the main triggering parameters of based on their corresponding FR values. Majority of high risk areas for rainfall-induced landslides were located along the southern portion belonging to Davao City and Kitao-tao and Arakan Municipalities. Validation using Receiver Operator Characteristic (ROC) revealed a model accuracy rate of 76.4% at 0.05 level. Hence, the LSI map generated can be a useful input in planning out projects for road repairs and maintenance along landslide prone areas so that cost, disturbances and exposure to such hazards may be minimized.

Hydropower remains crucial for renewable energy in the Philippines, particularly in rural areas with limited grid access. This study evaluates machine learning algorithms for short-term hydropower output prediction using limited data from a hydropower facility in Maramag, Bukidnon. Historical operational data spanning six months included hourly records of temperature, wet bulb temperature, dew point, precipitation, and power generation. Twelve machine learning models were trained and assessed using an 80:20 training-testing split. Feature engineering enhanced model robustness by identifying operational patterns and refining variables. Random Forest achieved the lowest MAE and strong overall performance with R-squared of 0.90 and lowest mean absolute error (MAE) of 56.71, followed by XGBoost (R2 = 0.889) and LightGBM (R2 = 0.908). k-Nearest Neighbors and Decision Tree also demonstrated effectiveness in data-limited environments. Traditional time series models like ARIMA performed poorly (R2 = -0.18), highlighting the importance of appropriate algorithm selection for non-linear hydropower data. Despite dataset constraints, results demonstrate that accessible machine learning algorithms can support energy planning and output estimation in rural hydropower systems with minimal instrumentation. This contributes to sustainable energy resource management and food-energy-water security objectives in vulnerable communities. Random Forest had the lowest MAE, while LightGBM achieved the highest R2; we prioritize RF due to lower absolute error.