Assessing Pollution with Heavy Metals and Its Impact on Population Health
Pollution is one of the most important issues currently affecting the global population and environment. Therefore, determining the zones where stringent measures should be taken is necessary. In this study, Principal Component Analysis (PCA), Factor Analysis (FA), and t-distributed Stochastic Neigh...
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2025-01-01
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| author | Youssef Saliba Alina Bărbulescu |
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| description | Pollution is one of the most important issues currently affecting the global population and environment. Therefore, determining the zones where stringent measures should be taken is necessary. In this study, Principal Component Analysis (PCA), Factor Analysis (FA), and t-distributed Stochastic Neighbor Embedding (t-SNE) were utilized for dimensionality reduction and clustering of data series containing the concentration of 10 heavy metals collected at 14 locations. The Hazard Quotient (<i>HQ</i>) and Hazard Index (<i>HI</i>) were utilized to determine the non-carcinogenic risk to the population in the studied zones. The highest concentrations of metals in the samples were those of Fe, Zn, Mn, and Cr. PCA indicated that Fe and Zn (Co and Cd) had the highest contribution on the first (second) Principal Component (PC). FA showed that the three-factor model is adequate for explaining the variability of pollutant concentrations. The factor loadings revealed the strength of association between variables and factors, e.g., 0.97 for Zn, 0.83 for Cr, and 0.99 for Co. <i>HQ</i> for ingestion, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>H</mi><mi>Q</mi></mrow><mrow><mi>i</mi><mi>n</mi><mi>g</mi></mrow></msub><mo>,</mo></mrow></semantics></math></inline-formula> was the highest for Fe (between 6.10 × 10<sup>−5</sup> and 2.57 × 10<sup>−4</sup>). <i>HQ</i> for inhalation, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>H</mi><mi>Q</mi></mrow><mrow><mi>i</mi><mi>n</mi><mi>h</mi></mrow></msub></mrow></semantics></math></inline-formula>, was the biggest for Mn (from 1.41 × 10<sup>−3</sup> to 1.95 × 10<sup>−3</sup>). <i>HI</i> varied in the interval [0.172, 0.573], indicating the absence of a non-carcinogenic risk. However, since values above 0.5 were determined at four sites, continuous monitoring of the pollution in the sampling locations is necessary. |
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| spelling | doaj-art-3df30b7633614aa198e276395fdf2ea42025-01-24T13:51:04ZengMDPI AGToxics2305-63042025-01-011315210.3390/toxics13010052Assessing Pollution with Heavy Metals and Its Impact on Population HealthYoussef Saliba0Alina Bărbulescu1Doctoral School, Technical University of Civil Engineering of Bucharest, 122-124 Bd. Lacul Tei, 020396 Bucharest, RomaniaDepartment of Civil Engineering, Transilvania University of Brașov, 5 Turnului Str., 500152 Brașov, RomaniaPollution is one of the most important issues currently affecting the global population and environment. Therefore, determining the zones where stringent measures should be taken is necessary. In this study, Principal Component Analysis (PCA), Factor Analysis (FA), and t-distributed Stochastic Neighbor Embedding (t-SNE) were utilized for dimensionality reduction and clustering of data series containing the concentration of 10 heavy metals collected at 14 locations. The Hazard Quotient (<i>HQ</i>) and Hazard Index (<i>HI</i>) were utilized to determine the non-carcinogenic risk to the population in the studied zones. The highest concentrations of metals in the samples were those of Fe, Zn, Mn, and Cr. PCA indicated that Fe and Zn (Co and Cd) had the highest contribution on the first (second) Principal Component (PC). FA showed that the three-factor model is adequate for explaining the variability of pollutant concentrations. The factor loadings revealed the strength of association between variables and factors, e.g., 0.97 for Zn, 0.83 for Cr, and 0.99 for Co. <i>HQ</i> for ingestion, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>H</mi><mi>Q</mi></mrow><mrow><mi>i</mi><mi>n</mi><mi>g</mi></mrow></msub><mo>,</mo></mrow></semantics></math></inline-formula> was the highest for Fe (between 6.10 × 10<sup>−5</sup> and 2.57 × 10<sup>−4</sup>). <i>HQ</i> for inhalation, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>H</mi><mi>Q</mi></mrow><mrow><mi>i</mi><mi>n</mi><mi>h</mi></mrow></msub></mrow></semantics></math></inline-formula>, was the biggest for Mn (from 1.41 × 10<sup>−3</sup> to 1.95 × 10<sup>−3</sup>). <i>HI</i> varied in the interval [0.172, 0.573], indicating the absence of a non-carcinogenic risk. However, since values above 0.5 were determined at four sites, continuous monitoring of the pollution in the sampling locations is necessary.https://www.mdpi.com/2305-6304/13/1/52multivariate analysispollution indicatorPCAt-SNEFA<i>HI</i> |
| spellingShingle | Youssef Saliba Alina Bărbulescu Assessing Pollution with Heavy Metals and Its Impact on Population Health Toxics multivariate analysis pollution indicator PCA t-SNE FA <i>HI</i> |
| title | Assessing Pollution with Heavy Metals and Its Impact on Population Health |
| title_full | Assessing Pollution with Heavy Metals and Its Impact on Population Health |
| title_fullStr | Assessing Pollution with Heavy Metals and Its Impact on Population Health |
| title_full_unstemmed | Assessing Pollution with Heavy Metals and Its Impact on Population Health |
| title_short | Assessing Pollution with Heavy Metals and Its Impact on Population Health |
| title_sort | assessing pollution with heavy metals and its impact on population health |
| topic | multivariate analysis pollution indicator PCA t-SNE FA <i>HI</i> |
| url | https://www.mdpi.com/2305-6304/13/1/52 |
| work_keys_str_mv | AT youssefsaliba assessingpollutionwithheavymetalsanditsimpactonpopulationhealth AT alinabarbulescu assessingpollutionwithheavymetalsanditsimpactonpopulationhealth |