The declining costs of photovoltaic technologies have accelerated the expansion of solar ianstallations in the Philippines, necessitating an effective detection method to delineate both utility-scale PV (UPV) and distributed PV (DPV) systems, which is essential for status monitoring and implementation of relevant programs by stakeholders and decision-makers. This study aims to detect and delineate PV installations in Central Luzon using satellite imagery sources with different spatial resolutions (Sentinel-2, Planetscope, and Kompsat-3) and machine learning, with the aid of open-source GIS and remote sensing software. A semi-automated approach combining pixel-based classification (PBC) and object-based classification (OBC) was introduced to enhance accuracy. Training and validation data were extracted from the satellite images. The study also implemented a post-processing procedure using a set of spectral rules to refine the classification results. The performance for each classification approach (PBC and OBC) was evaluated using the three classifiers: Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The findings revealed that all image sources showed similar superior performance in classifying UPVs, with Sentinel-2 achieving the highest F1-score of 96.83%. However, for DPVs, Kompsat-3 had the highest number of detected installations (75) and achieved delineation accuracies of 0.42 (Pampanga) and 0.5 (Tarlac), with the latter meeting the threshold of 0.5 despite the limited coverage of the study area. In contrast, Sentinel-2 struggled to detect PVs smaller than 7 pixels.vi Estimated power capacities showed errors of 7% and 9.4% for UPVs and 10% and 15.6% for DPVs when using STC irradiance and average irradiance, respectively. This research demonstrates the effectiveness of integrating remote sensing, and machine learning for PV mapping, providing insights for better monitoring and management of solar energy infrastructure. Future works may explore applying better detection algorithms and postprocessing methods to further reduce misclassifications in urban environments.

This study presents a simple and environmentally friendly method for preparing homogeneous, small-particle-size, crystalline nickel (Ni) and cobalt (Co) oxide-based precursors from spent nickel-cobalt-aluminum (NCA) cathode materials. By selectively removing lithium (Li) and aluminum (Al), the study aims to create a facile and sustainable alternative process to obtain a mixed Ni-Co oxide precursors as alternative to conventional methods like co-precipitation, addressing the growing spent or waste LiBs from increasing numbers of electric vehicle (EV) in the Philippines. The developed process involved metal removal, powder preparation with different particle sizes, and screen-printing to fabricate Ni-Co oxide films for potential application as catalysts in lithium-oxygen batteries (LOBs). Key results include achieving a high-purity Al removal (99.91%), a Ni:Co ratio of 85:15, and mixed Ni-Co oxide phases as analyzed via X-ray diffraction, Raman spectroscopy, XRF and ICP-OES. Ball-milling effectively reduced particle size, about 171.7 nm average particle size, yielding porous and homogeneous screen-printed films with a porosity of about 43.33%. SEM-EDS analysis revealed good and homogeneous deposition, with particle size influencing the film thickness. The milled powder with 171.7 nm, 296.5 nm, and 537 nm particle sizes produced thin films averaging 5.50 µm, 7.06 µm, and 7.93 µm on carbon cathode substrates, respectively.

In binary geothermal systems, extraction of heat from the brine should be properly managed to prevent unwanted deposition of scales in the heat exchanger units, exiting brine pipeline and reinjection wells. This study assessed a polymer-based scale
inhibitor as a viable alternative in minimizing the deposition of silica and other scales while allowing lower outlet temperature to further increase the projected output of the plant. A sidestream test skid was used to mimic the conditions of the binary plant which focused on attaining a lower outlet temperature (from 140°C to 100°C) and holding the brine for at least 90 mins. Inhibitor treatment resulted in a 95% monomeric silica retention versus 80% for the untreated. Deposit weight density analysis also showed lower deposits in the treated line (38 g/ft2 at 100°C) compared to the inlet (55 g/ft2 at 140°C). An increase in induction time was also observed in the treated line after retention and capability to prevent metal sulfides and silicates was confirmed using SEM/EDS. It was projected than an additional 4.9 MW can be achieved from the outlet temperature adjustment.

The occurrence of microplastics in freshwater systems is of growing concern and being studied worldwide. Rivers serve as channels for carrying microplastics to other water bodies, such as lakes and oceans. In the Philippines, Laguna Lake is considered to be the largest lake and known for its many uses. One of its tributaries is the Taguig River. This study is the first to evaluate the abundance, characteristics, and distribution of microplastics along Taguig River to Laguna Lake in surface water, water column, and sediments during dry and wet seasons. Samples were collected, extracted, and examined under a 40x magnification microscope according to size, color, and shape. For the dry season, results showed that the concentrations of microplastics are 107-390 particles/m3 in surface water, 47-370 particles/m3 in water column, and 76-119 particles/kg in
sediments. For the wet season, concentrations are 60-103 particles/m3 in surface water, 30-77 particles/m3 in water column, and 43-129 particles/kg in sediments. The concentration of microplastics is higher during the dry season than the wet season. The average ratio of microplastic count in water column to surface water is 0.57. The dominant colors found are transparent, blue, and white. Microplastics in the form of fiber and films are abundant in water samples and sediments, respectively. Representative samples analyzed using a Fourier-Transform Infrared (FTIR) spectroscopy revealed that the polymer types present are polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), and polystyrene (PS). Further, the total microplastic pollution load in water samples of Taguig River contributing to Laguna Lake is approximately 2-61 particles/s.

The capacity of an Asphalt Pavement to resist loads depends on its temperature due to its viscoelasticity. Because of this, measurements gathered from field tests vary due to different temperatures experienced by the pavement. Temperature correction procedures are developed by different countries to properly address this problem. However, in the Philippine setting, such temperature correction procedures are yet to be established. A reference temperature should also be set to standardize FWD measurements, however, no reference temperature is yet to be established for perusal in the Philippines. The study aimed to bridge this gap by conducting an experiment that collected temperatures at three (3) set depths; pavement surface, mid depth, and third depth on a test pavement located at the Ninoy Aquino International Airport (NAIA). Using these measurements, a temperature prediction model based on polynomial regression analysis and elastic net regularization technique was developed. The application of the elastic net regularization technique minimized highly correlated pavement temperature factors to simplify model inputs and be used with ease during FWD tests. The model performed better than existing models with an r-squared value of 0.932 and 0.864 at mid and third depths respectively with errors lower than existing models in comparison. It was recommended to utilize the temperature prediction model to develop standard procedures in using the Falling Weight Deflectometer (FWD) in monitoring the structural capacity of pavements and to establish a local reference temperature for temperature correction procedure.