Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF)
Heavy metals in road dust pose a significant threat to human health. This study investigated the concentrations, patterns, and sources of eight hazardous heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) in the street dust of Zhengzhou city of PR China. Fifty-eight samples of road dust were analyzed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/12/5/614 |
| _version_ | 1797534740818952192 |
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| author | Muhammad Faisal Zening Wu Huiliang Wang Zafar Hussain Chenyang Shen |
| author_facet | Muhammad Faisal Zening Wu Huiliang Wang Zafar Hussain Chenyang Shen |
| author_sort | Muhammad Faisal |
| collection | DOAJ |
| description | Heavy metals in road dust pose a significant threat to human health. This study investigated the concentrations, patterns, and sources of eight hazardous heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) in the street dust of Zhengzhou city of PR China. Fifty-eight samples of road dust were analyzed based on three methods of risk assessment, i.e., Geo-Accumulation Index (I<sub>geo</sub>), Potential Ecological Risk Assessment (RI), and Nemerow Synthetic Pollution Index (PI<sub>N</sub>). The results exhibited higher concentrations of Hg and Cd 14 and 7 times higher than their background values, respectively. I<sub>geo</sub> showed the risks of contamination in a range of unpolluted (Cr, Ni) to strongly polluted (Hg and Cd) categories. RI came up with the contamination ranges from low (Cr, Ni, Cu, Zn, As, and Pb) to extreme (Cd and Hg) risk of contamination. The risk of contamination based on PI<sub>N</sub> was from safe (Cu, As, and Pb) to seriously high (Cd and Hg). The results yielded by PI<sub>N</sub> indicated the extreme risk of Cd and Hg in the city. Positive Matrix Factorization was used to identify the sources of contamination. Factor 1 (vehicular exhaust), Factor 2 (coal combustion), Factor 3 (metal industry), and Factor 4 (anthropogenic activities), respectively, contributed 14.63%, 35.34%, 36.14%, and 13.87% of total heavy metal pollution. Metal’s presence in the dust is a direct health risk for humans and warrants immediate and effective pollution control and prevention measures in the city. |
| first_indexed | 2024-03-10T11:34:44Z |
| format | Article |
| id | doaj.art-888231660b4b4a85853b78851daee1d6 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T11:34:44Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-888231660b4b4a85853b78851daee1d62023-11-21T18:57:38ZengMDPI AGAtmosphere2073-44332021-05-0112561410.3390/atmos12050614Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF)Muhammad Faisal0Zening Wu1Huiliang Wang2Zafar Hussain3Chenyang Shen4College of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, ChinaCollege of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, ChinaCollege of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, ChinaCollege of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, ChinaCollege of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, ChinaHeavy metals in road dust pose a significant threat to human health. This study investigated the concentrations, patterns, and sources of eight hazardous heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) in the street dust of Zhengzhou city of PR China. Fifty-eight samples of road dust were analyzed based on three methods of risk assessment, i.e., Geo-Accumulation Index (I<sub>geo</sub>), Potential Ecological Risk Assessment (RI), and Nemerow Synthetic Pollution Index (PI<sub>N</sub>). The results exhibited higher concentrations of Hg and Cd 14 and 7 times higher than their background values, respectively. I<sub>geo</sub> showed the risks of contamination in a range of unpolluted (Cr, Ni) to strongly polluted (Hg and Cd) categories. RI came up with the contamination ranges from low (Cr, Ni, Cu, Zn, As, and Pb) to extreme (Cd and Hg) risk of contamination. The risk of contamination based on PI<sub>N</sub> was from safe (Cu, As, and Pb) to seriously high (Cd and Hg). The results yielded by PI<sub>N</sub> indicated the extreme risk of Cd and Hg in the city. Positive Matrix Factorization was used to identify the sources of contamination. Factor 1 (vehicular exhaust), Factor 2 (coal combustion), Factor 3 (metal industry), and Factor 4 (anthropogenic activities), respectively, contributed 14.63%, 35.34%, 36.14%, and 13.87% of total heavy metal pollution. Metal’s presence in the dust is a direct health risk for humans and warrants immediate and effective pollution control and prevention measures in the city.https://www.mdpi.com/2073-4433/12/5/614road dustheavy metalspositive matrix factorizationgeochemical mappingtraditional risk assessment |
| spellingShingle | Muhammad Faisal Zening Wu Huiliang Wang Zafar Hussain Chenyang Shen Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF) Atmosphere road dust heavy metals positive matrix factorization geochemical mapping traditional risk assessment |
| title | Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF) |
| title_full | Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF) |
| title_fullStr | Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF) |
| title_full_unstemmed | Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF) |
| title_short | Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF) |
| title_sort | geochemical mapping risk assessment and source identification of heavy metals in road dust using positive matrix factorization pmf |
| topic | road dust heavy metals positive matrix factorization geochemical mapping traditional risk assessment |
| url | https://www.mdpi.com/2073-4433/12/5/614 |
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