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. 2022 Jun 21;10(7):342.
doi: 10.3390/toxics10070342.

In Situ Measurements of Domestic Water Quality and Health Risks by Elevated Concentration of Heavy Metals and Metalloids Using Monte Carlo and MLGI Methods

Delia B Senoro  1   2   3   4   5 , Kevin Lawrence M de Jesus  1   2   3 , Ronnel C Nolos  1   5   6 , Ma Rowela L Lamac  5   6 , Khainah M Deseo  1   5 , Carlito B Tabelin  7
Affiliations

In Situ Measurements of Domestic Water Quality and Health Risks by Elevated Concentration of Heavy Metals and Metalloids Using Monte Carlo and MLGI Methods

Delia B Senoro et al. Toxics. .

Abstract

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.

Keywords: carcinogenic risk; domestic water; machine learning; metal pollution; spatial distribution maps.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
SA of LTCR model (adults) for As in water.
Figure A2
Figure A2
SA of LTCR model (adults) for Ni in water.
Figure A3
Figure A3
SA of LTCR model (adults) for Pb in water.
Figure A4
Figure A4
SA of LTCR model (children) for As in water.
Figure A5
Figure A5
SA of LTCR model (children) for Ni in water.
Figure A6
Figure A6
SA of LTCR model (children) for Pb in water.
Figure A7
Figure A7
Correlation plots for NN-PSO simulations of MPI for (a) TW, (b) GW, and (c) WRS.
Figure A8
Figure A8
Correlation plots for NN-PSO simulations of NPI for (a) TW, (b) GW, and (c) WRS.
Figure A9
Figure A9
Correlation plots for NN-PSO simulations of HI (adults) for (a) TW, (b) GW, and (c) WRS.
Figure A10
Figure A10
Correlation plots for NN-PSO simulations of HI (children) for (a) TW, (b) GW, and (c) WRS.
Figure A11
Figure A11
Correlation plots for NN-PSO simulations of TCR (adults) for (a) TW, (b) GW, and (c) WRS.
Figure A12
Figure A12
Correlation plots for NN-PSO simulations of TCR (children) for (a) TW, (b) GW, and (c) WRS.
Figure 1
Figure 1
A map of the Philippines showing Marinduque Island as a zoomed-in inset, including domestic water sampling locations for this study.
Figure 2
Figure 2
Analysis of HMMs using Accusensing MAS G1 and pXRF.
Figure 3
Figure 3
Average HMM concentrations in all DW samples.
Figure 4
Figure 4
Mean CDI of metals in water from WRS, groundwater, and tap water.
Figure 5
Figure 5
Distribution of TCR in domestic water (DW) sources for (a) adults and (b) children.
Figure 6
Figure 6
Predicted probability of TCR (adults) for As in water.
Figure 7
Figure 7
Predicted probability of TCR (Adult) for Ni in water.
Figure 8
Figure 8
Predicted probability of TCR (Adult) for Pb in water.
Figure 9
Figure 9
Predicted probability of TCR (children) for As in water.
Figure 10
Figure 10
Predicted probability of TCR (children) for Ni in water.
Figure 11
Figure 11
Predicted probability of TCR (children) for Pb in water.
Figure 12
Figure 12
Correlation matrix plots of WQ parameters obtained for (a) TW samples, (b) GW samples, and (c) WRS samples.
Figure 13
Figure 13
Cluster analysis dendrogram of HMMs in (a) WRS, (b) GW, and (c) TW.
Figure 14
Figure 14
Spatial maps of MPI developed using MLGI approach for (a) TW samples, (b) GW samples, and (c) WRS samples.
Figure 15
Figure 15
Spatial maps of NPI developed using MLGI approach for (a) TW samples, (b) GW samples, and (c) WRS samples.
Figure 16
Figure 16
Spatial maps of HI (adults) developed using MLGI approach for (a) TW samples, (b) GW samples, and (c) WRS samples.
Figure 17
Figure 17
Spatial maps of HI (children) developed using MLGI approach for (a) TW samples, (b) GW samples, and (c) WRS samples.
Figure 18
Figure 18
Spatial maps of TCR (adult) developed using MLGI approach for (a) TW samples, (b) GW samples, and (c) WRS samples.
Figure 19
Figure 19
Spatial maps of TCR (child) developed using MLGI approach for (a) TW samples, (b) GW samples, and (c) WRS samples.
Figure 20
Figure 20
Akaike information criterion (AIC) values for pollution and risk indices in (a) TW, (b) GW, and (c) WRS.
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