Hazard ranking of wastewater sources in a highly polluted river in northern Taiwan by using positive matrix factorization with metal elements
Abstract Improving water quality is a critical issue worldwide. However, the general parameters (i.e., temperature, pH, turbidity, total solids, fecal coliform, dissolved oxygen, biochemical oxygen demand, phosphates, and nitrates) used in water quality index estimations are unable to identify pollu...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-06-01
|
| Series: | Sustainable Environment Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42834-022-00144-9 |
| _version_ | 1818235155746127872 |
|---|---|
| author | Pei-Yuan Hsieh Huan-Chun Lin Gen-Shuh Wang Yuan-Jeng Hsu Yi-Ju Chen Tzu-Hui Wang Ren-De Wang Chun-Yu Kuo Di-Wen Wang Ho-Tang Liao Chang-Fu Wu |
| author_facet | Pei-Yuan Hsieh Huan-Chun Lin Gen-Shuh Wang Yuan-Jeng Hsu Yi-Ju Chen Tzu-Hui Wang Ren-De Wang Chun-Yu Kuo Di-Wen Wang Ho-Tang Liao Chang-Fu Wu |
| author_sort | Pei-Yuan Hsieh |
| collection | DOAJ |
| description | Abstract Improving water quality is a critical issue worldwide. However, the general parameters (i.e., temperature, pH, turbidity, total solids, fecal coliform, dissolved oxygen, biochemical oxygen demand, phosphates, and nitrates) used in water quality index estimations are unable to identify pollution from industrial wastewater. This study investigated pollution sources at a river pollution hotspot by using the positive matrix factorization (PMF) model. A two-phase sampling collection along a highly polluted river in northern Taiwan was designed. The sampling spots were distributed along the river in Phase I to monitor the spatial variation of river pollutants. A pollution hotspot was determined based on two indices, namely the summed concentrations of metal elements and a metal index (MI). In Phase II, the river water samples were collected from the hotspot twice daily over 30 consecutive days to monitor the temporal variation of river pollutants. Source profiles of metal elements were obtained during the monitoring period. The Phase II samples were then factorized using the PMF model. Factor profiles retrieved from the PMF model were further assigned to industrial categories through Pearson correlation coefficients and hierarchical classification. The results indicated that the main pollution source was bare printed circuit boards (BPCB), which contributed up to 92% of the copper in the pollution hotspot. In terms of MI apportionment of 11 metals related to health effects, BPCB contributed 91% of the MI in high pollution events. Overall, the MI apportionment provides linkages between pollution level and human health. This is an evidence for policymakers that the regulation of the effluents of BPCB is an effective means to controlling copper concentrations and thus improving water quality in the study area. |
| first_indexed | 2024-12-12T11:49:29Z |
| format | Article |
| id | doaj.art-cac5f413fb044c9dbcae9971dcd331b3 |
| institution | Directory Open Access Journal |
| issn | 2468-2039 |
| language | English |
| last_indexed | 2024-12-12T11:49:29Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Sustainable Environment Research |
| spelling | doaj.art-cac5f413fb044c9dbcae9971dcd331b32022-12-22T00:25:22ZengBMCSustainable Environment Research2468-20392022-06-0132111010.1186/s42834-022-00144-9Hazard ranking of wastewater sources in a highly polluted river in northern Taiwan by using positive matrix factorization with metal elementsPei-Yuan Hsieh0Huan-Chun Lin1Gen-Shuh Wang2Yuan-Jeng Hsu3Yi-Ju Chen4Tzu-Hui Wang5Ren-De Wang6Chun-Yu Kuo7Di-Wen Wang8Ho-Tang Liao9Chang-Fu Wu10Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityEnvironmental Analysis Laboratory, Environmental Protection AdministrationEnvironmental Analysis Laboratory, Environmental Protection AdministrationEnvironmental Analysis Laboratory, Environmental Protection AdministrationEnvironmental Analysis Laboratory, Environmental Protection AdministrationEnvironmental Analysis Laboratory, Environmental Protection AdministrationEnvironmental Analysis Laboratory, Environmental Protection AdministrationInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityAbstract Improving water quality is a critical issue worldwide. However, the general parameters (i.e., temperature, pH, turbidity, total solids, fecal coliform, dissolved oxygen, biochemical oxygen demand, phosphates, and nitrates) used in water quality index estimations are unable to identify pollution from industrial wastewater. This study investigated pollution sources at a river pollution hotspot by using the positive matrix factorization (PMF) model. A two-phase sampling collection along a highly polluted river in northern Taiwan was designed. The sampling spots were distributed along the river in Phase I to monitor the spatial variation of river pollutants. A pollution hotspot was determined based on two indices, namely the summed concentrations of metal elements and a metal index (MI). In Phase II, the river water samples were collected from the hotspot twice daily over 30 consecutive days to monitor the temporal variation of river pollutants. Source profiles of metal elements were obtained during the monitoring period. The Phase II samples were then factorized using the PMF model. Factor profiles retrieved from the PMF model were further assigned to industrial categories through Pearson correlation coefficients and hierarchical classification. The results indicated that the main pollution source was bare printed circuit boards (BPCB), which contributed up to 92% of the copper in the pollution hotspot. In terms of MI apportionment of 11 metals related to health effects, BPCB contributed 91% of the MI in high pollution events. Overall, the MI apportionment provides linkages between pollution level and human health. This is an evidence for policymakers that the regulation of the effluents of BPCB is an effective means to controlling copper concentrations and thus improving water quality in the study area.https://doi.org/10.1186/s42834-022-00144-9River water pollutionIndustrial wastewaterSource profileSource apportionment |
| spellingShingle | Pei-Yuan Hsieh Huan-Chun Lin Gen-Shuh Wang Yuan-Jeng Hsu Yi-Ju Chen Tzu-Hui Wang Ren-De Wang Chun-Yu Kuo Di-Wen Wang Ho-Tang Liao Chang-Fu Wu Hazard ranking of wastewater sources in a highly polluted river in northern Taiwan by using positive matrix factorization with metal elements Sustainable Environment Research River water pollution Industrial wastewater Source profile Source apportionment |
| title | Hazard ranking of wastewater sources in a highly polluted river in northern Taiwan by using positive matrix factorization with metal elements |
| title_full | Hazard ranking of wastewater sources in a highly polluted river in northern Taiwan by using positive matrix factorization with metal elements |
| title_fullStr | Hazard ranking of wastewater sources in a highly polluted river in northern Taiwan by using positive matrix factorization with metal elements |
| title_full_unstemmed | Hazard ranking of wastewater sources in a highly polluted river in northern Taiwan by using positive matrix factorization with metal elements |
| title_short | Hazard ranking of wastewater sources in a highly polluted river in northern Taiwan by using positive matrix factorization with metal elements |
| title_sort | hazard ranking of wastewater sources in a highly polluted river in northern taiwan by using positive matrix factorization with metal elements |
| topic | River water pollution Industrial wastewater Source profile Source apportionment |
| url | https://doi.org/10.1186/s42834-022-00144-9 |
| work_keys_str_mv | AT peiyuanhsieh hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT huanchunlin hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT genshuhwang hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT yuanjenghsu hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT yijuchen hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT tzuhuiwang hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT rendewang hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT chunyukuo hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT diwenwang hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT hotangliao hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements AT changfuwu hazardrankingofwastewatersourcesinahighlypollutedriverinnortherntaiwanbyusingpositivematrixfactorizationwithmetalelements |