Pollution and Risk Evaluation of Toxic Metals and Metalloid in Water Resources of San Jose, Occidental Mindoro, Philippines

Senoro, Delia B.; Jesus, Kevin Lawrence M. de; Monjardin, Cris Edward F.

doi:10.3390/su15043667

Cited by 7 publications

(4 citation statements)

References 131 publications

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“…The new model, as a result of calibration to adjust default calibration values and meet site specific factors, has been labelled as ‘soil ziploc mode’. After this, the portable XRF is ready for elemental analysis and becomes a reliable instrument for metal detection in this study for various media, and it has been used in various studies [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. The 77 samples were analyzed for the presence of ten (10) metals, such as arsenic (As), barium (Ba), cadmium (Cd), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), mercury (Hg), nickel (Ni), and zinc (Zn).…”

Section: Methodsmentioning

confidence: 99%

Detection of Heavy Metals, Their Distribution in Tilapia spp., and Health Risks Assessment

Mendoza

Nolos

Villaflores

et al. 2023

Toxics

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Concentrations of heavy metals (HMs) were assessed in Tilapia spp. from selected communities in Calapan City, Philippines. Eleven (11) inland farmed tilapia samples were collected and analyzed for HMs concentration using X-ray fluorescence (XRF). The 11 fish samples were cut into seven pieces, according to the fish body parts, constituting a total of 77 samples. These fish samples were then labeled as bone, fins, head, meat, skin, and viscera. Results showed that the mean concentration of Cd in all parts of tilapia exceeded the Food and Agriculture Organization/World Health Organization (FAO/WHO) limits. The highest concentration was recorded in the fins, which was sevenfold higher than the limit. The trend of the mean concentration of Cd in different parts of tilapia was fins > viscera > skin > tail > head > meat > bone. The target hazard quotient (THQ) recorded a value less than 1. This means that the population exposed to tilapia, within the area where fish samples originated, were not at risk to non-carcinogens. The concentrations of Cu, Pb, Mn, Hg, and Zn in different parts, particularly in skin, fins, and viscera, also exceeded the FAO/WHO limits. The calculated cancer risk (CR) in consuming the fish skin, meat, fins, bone, viscera, and head was higher than the USEPA limit. This indicated a possible carcinogenic risk when consumed regularly. Most of the correlations observed between HMs in various parts of the tilapia had positive (direct) relationships, which were attributed to the HM toxicity target organ characteristics. Results of the principal component analysis (PCA) showed that most of the dominating HMs recorded in tilapia were attributable to anthropogenic activities and natural weathering within the watershed of agricultural areas. The agriculture area comprises about 86.83% of the overall land area of Calapan City. The identified carcinogenic risks were associated with Cd. Therefore, regular monitoring of HMs in inland fishes, their habitat, and surface water quality shall be carried out. This information is useful in creating strategies in metals concentration monitoring, health risks reduction program, and relevant guidelines that would reduce the accumulation of HM in fish.

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Section: Methodsmentioning

confidence: 99%

Detection of Heavy Metals, Their Distribution in Tilapia spp., and Health Risks Assessment

Mendoza

Nolos

Villaflores

et al. 2023

Toxics

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“…The XRF device was extremely beneficial for measuring soil Mn as the study site is on a remote island without an appropriate analytical laboratory. It can analyze metal concentrations in soil samples, though the focus of this study is Mn, with a confidence level of 99.7% [51] and has been employed for a range of environmental monitoring studies [52][53][54].…”

Section: Soil and Surface Water Sampling In Mogpog And Boac Municipal...mentioning

confidence: 99%

Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water

Monjardin

Power

Senoro

et al. 2023

Water

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This study explored the application of machine learning, specifically artificial neural network (ANN), to create prediction models for manganese (Mn) concentration in soil and surface water (SW) on the island province with two open mine pits overflowing to two major rivers that experienced mining disasters. The two ANN models were created to predict Mn concentrations in soil and SW from 12 and 14 input parameters for soil and SW, respectively. These input parameters were extracted from extensive field data collected at the site during sampling program in 2019, 2021, 2022, and initially processed with spatial analysis via geographic information system (GIS). All datasets were then divided for model training and validation, using 85% and 15% ratio, respectively. Performance evaluation of each model with mean absolute percentage error (MAPE) and root mean squared error (RMSE) confirmed the accuracy of both models. The soil Mn model achieved MAPE and RMSE values of 2.01% and 23.98, respectively. The SW Mn model was split into two models based on SW Mn values within the 0–1 mg/L range and >1 mg/L range. The SW Mn model for >1 mg/L performed better with MAPE and RMSE of 4.61% and 0.17, respectively. Feature reduction was also conducted to identify how the models will perform if some input parameters were excluded. Result showed sufficient accuracy can still be obtained with the removal of 4–5 input parameters. This study and these models highlight the benefit of ANN to the scientific community and government units, for predicting Mn concentration, of similar environmental conditions.

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“…In addition, several industries, such as those using electroplating and copper mining processes generate wastewater with (II) concentrations that are way above the toxic concentrations [2]. This increase in heavy metals found in water is alarming due to the adverse effects of heavy metals if ingested [3]. Furthermore, it has also been found that around half of Filipinos depend on groundwater as their primary source of drinking water [4].…”

Section: Introductionmentioning

confidence: 99%

Fabrication of Paper-Based Silver Nanoparticle (AgNP) Sensors for Smartphone-Based Colorimetric Detection of Cu (II) in Water

Gomeceria,

Miranda,

Lopez

et al. 2024

MSF

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Detection of heavy metals in water has long been a key area of study due to the adverse health effects these substances may bring. Multiple methods of detecting heavy metals have already been established. Though these methods are highly selective and can detect heavy metals in trace amounts, they commonly require specialized equipment. Thus, producing an inexpensive, reliable, and convenient sensor that could be used for point-of-need applications is of great interest. This study focuses on fabricating paper-based silver nanoparticle (AgNP) sensors for the smartphone-based colorimetric detection of Cu2+ ions in water. Polymer-decorated AgNPs functionalized by chitosan, glutaraldehyde, and polyethyleneimine were used as the main sensing mechanism for the paper-based sensors. Various fabrication methods were tested, and the optimal fabrication method was through the rectangular soak method with a total of 5 coatings as it produced the most uniform sensors. The calibration curve was studied over concentrations from 0.5 mM to 50 mM of Cu2+ across multiple parameters. It was found that there was a linear correlation between the Euclidean distance measured in reference to the blank filter paper against the concentration of copper in the analyte. The calibration curve exhibited a dynamic linear range between 2 mM to 28 mM of Cu2+ with R2 = 0.99789. The LOD and LOQ were reported at 94.9438 ppm and 316.4793 ppm, respectively. Lastly, selectivity studies were also performed to determine the sensor’s response to other metal ions. It was found that the response of the sensor to Cu2+ was significantly different from those elicited by Ni2+, Cd+, Mn2+, Ca2+, Mg2+, Sn2+, K+, Cr3+, Al3+, Ba2+, Na+, Zn2+, Fe3+, and Fe2+. The study demonstrated its strong potential as rapid on-site detection method for Cu (II) in industrial wastewater.

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Pollution and Risk Evaluation of Toxic Metals and Metalloid in Water Resources of San Jose, Occidental Mindoro, Philippines

Cited by 7 publications

References 131 publications

Detection of Heavy Metals, Their Distribution in Tilapia spp., and Health Risks Assessment

Detection of Heavy Metals, Their Distribution in Tilapia spp., and Health Risks Assessment

Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water

Fabrication of Paper-Based Silver Nanoparticle (AgNP) Sensors for Smartphone-Based Colorimetric Detection of Cu (II) in Water

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