This paper presents the effects of flooding on the accumulation of manganese (Mn) in soils within proximity of the Boac and Mogpog rivers in Marinduque of The Philippines. Marinduque, an island province in the Philippines, experienced two catastrophic tailings storage facility (TSF) failures in the 1990s that released sulfide-rich tailings into the two major rivers. The Philippines experiences 21–23 typhoons every year, 11 of which pass thru Marinduque that causing inundation of floodplain areas in the province. A flood hazard map developed using LiDAR DEM was utilized for the Boac and Mogpog rivers for an accurate representation of flooding events. A portable X-ray fluorescence spectrometer (pXRF) and a Hannah multi-parameter device were used for the on-site analyses of Mn concentration and water physico-chemical properties, respectively. Spatial grid mapping with zonal statistics was employed for a comprehensive analysis of all the data collected and processed. Correlation analysis was carried out on Mn concentrations in soil and surface water, electrical conductivity (EC), total dissolved solids (TDS), pH, temperature, curve number (CN), and flood heights. The curve number indicates the runoff response characteristic of the Mogpog-Boac River basin. The results show that 40% of the total floodplain area of Boac and Mogpog were subjected to high hazards with flood heights above 1.5 m. The Mn content of soils had a statistically significant moderate positive correlation with flood height (r = 0.458) and a moderate negative correlation with pH (r = −0.438). This condition suggested that more extensive flooding promotes Mn contamination of floodplain soils in the two rivers, the source of which includes the mobilization of Mn-bearing silt, sediments, and mine drainage from the abandoned mine pits and TSFs. There is also a strong negative correlation between pH and Mn concentrations in surface water, a relationship attributed to the solubilization of Mn-bearing precipitates based on geochemical modeling results. Using Muller’s geo-accumulation index, 77.5% of the total floodplain of the two rivers was identified as “moderately contaminated” with an average Mn soil content of 3.4% by weight (34,000 mg/kg). The Mn contamination map of floodplain soils in the Mogpog and Boac rivers described in this study could guide relevant regional, national, and local government agencies in planning appropriate intervention, mitigation, remediation, and rehabilitation strategies to limit human exposure to highly contaminated areas.

The domestic water (DW) quality of an island province in the Philippines that experienced two major mining disasters in the 1990s was assessed and evaluated in 2021 utilizing the heavy metals pollution index (MPI), Nemerow’s pollution index (NPI), and the total carcinogenic risk (TCR) index. The island province sources its DW supply from groundwater (GW), surface water (SW), tap water (TP), and water refilling stations (WRS). This DW supply is used for drinking and cooking by the population. In situ analyses were carried out using an Olympus Vanta X-ray fluorescence spectrometer (XRF) and Accusensing Metals Analysis System (MAS) G1 and the target heavy metals and metalloids (HMM) were arsenic (As), barium (Ba), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn). The carcinogenic risk was evaluated using the Monte Carlo (MC) method while a machine learning geostatistical interpolation (MLGI) technique was employed to create spatial maps of the metal concentrations and health risk indices. The MPI values calculated at all sampling locations for all water samples indicated a high pollution. Additionally, the NPI values computed at all sampling locations for all DW samples were categorized as “highly polluted”. The results showed that the health quotient indices (HQI) for As and Pb were significantly greater than 1 in all water sources, indicating a probable significant health risk (HR) to the population of the island province. Additionally, As exhibited the highest carcinogenic risk (CR), which was observed in TW samples. This accounted for 89.7% of the total CR observed in TW. Furthermore, all sampling locations exceeded the recommended maximum threshold level of 1.0 × 10⁻⁴ by the USEPA. Spatial distribution maps of the contaminant concentrations and health risks provide valuable information to households and guide local government units as well as regional and national agencies in developing strategic interventions to improve DW quality in the island province.

This article discusses the assessment of groundwater quality using a hybrid technique that would aid in the convenience of groundwater (GW) quality monitoring. Twenty eight (28) GW samples representing 62 barangays in Calapan City, Oriental Mindoro, Philippines were analyzed for their physicochemical characteristics and heavy metal (HM) concentrations. The 28 GW samples were collected at suburban sites identified by the coordinates produced by Global Positioning System Montana 680. The analysis of heavy metal concentrations was conducted onsite using portable handheld X-Ray Fluorescence (pXRF) Spectrometry. Hybrid machine learning—geostatistical interpolation (MLGI) method, specific to neural network particle swarm optimization with Empirical Bayesian Kriging (NN-PSO+EBK), was employed for data integration, GW quality spatial assessment and monitoring. Spatial map of metals concentration was produced using the NN-PSO-EBK. Another, spot map was created for observed metals concentration and was compared to the spatial maps. Results showed that the created maps recorded significant results based on its MSEs with values such as 1.404 × 10⁻⁴, 5.42 × 10⁻⁵, 6.26 × 10⁻⁴, 3.7 × 10⁻⁶, 4.141 × 10⁻⁴ for Ba, Cu, Fe, Mn, Zn, respectively. Also, cross-validation of the observed and predicted values resulted to R values range within 0.934–0.994 which means almost accurate. Based on these results, it can be stated that the technique is efficient for groundwater quality monitoring. Utilization of this technique could be useful in regular and efficient GW quality monitoring.

Limited monitoring activities to assess data on heavy metal (HM) concentration contribute to worldwide concern for the environmental quality and the degree of toxicants in areas where there are elevated metals concentrations. Hence, this study used in-situ physicochemical parameters to the limited data on HM concentration in SW and GW. The site of the study was Marinduque Island Province in the Philippines, which experienced two mining disasters. Prediction model results showed that the SW models during the dry and wet seasons recorded a mean squared error (MSE) ranging from 6 × 10⁻⁷ to 0.070276. The GW models recorded a range from 5 × 10⁻⁸ to 0.045373, all of which were approaching the ideal MSE value of 0. Kling–Gupta efficiency values of developed models were all greater than 0.95. The developed neural network-particle swarm optimization (NN-PSO) models for SW and GW were compared to linear and support vector machine (SVM) models and previously published deterministic and artificial intelligence (AI) models. The findings indicated that the developed NN-PSO models are superior to the developed linear and SVM models, up to 1.60 and 1.40 times greater than the best model observed created by linear and SVM models for SW and GW, respectively. The developed models were also on par with previously published deterministic and AI-based models considering their prediction capability. Sensitivity analysis using Olden’s connection weights approach showed that pH influenced the concentration of HM significantly. Established on the research findings, it can be stated that the NN-PSO is an effective and practical approach in the prediction of HM concentration in water resources that contributes a solution to the limited HM concentration monitored data.

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

The municipality of Romblon in the Philippines is an island known for its marble industry. The subsurface of the Philippines is known for its limestone. The production of marble into slab, tiles, and novelty items requires heavy equipment to cut rocks and boulders. The finishing of marble requires polishing to smoothen the surface. During the manufacturing process, massive amounts of particulates and slurry are produced, and with a lack of technology and human expertise, the environment can be adversely affected. Hence, this study assessed and monitored the environmental conditions in the municipality of Romblon, particularly the soils and sediments, which were affected due to uncontrolled discharges and particulates deposition. A total of fifty-six soil and twenty-three sediment samples were collected and used to estimate the metal and metalloid (MM) concentrations in the whole area using a neural network-particle swarm optimization inverse distance weighting model (NN-PSO). There were nine MMs; e.g., As, Cr, Ni, Pb, Cu, Ba, Mn, Zn and Fe, with significant concentrations detected in the area in both soils and sediments. The geo-accumulation index was computed to assess the level of contamination in the area, and only the soil exhibited contamination with zinc, while others were still on a safe level. Nemerow’s pollution index (NPI) was calculated for the samples collected, and soil was evaluated and seen to have a light pollution level, while sediment was considered as “clean”. Furthermore, the single ecological risk (Er) index for both soil and sediment samples was considered to be a low pollution risk because all values of Er were less than 40.