Research on evaluating water pollution determinants using multiple logistic regression
Water pollution is a pivotal challenge, underpinning urgent conversations around environmental sustainability, public health, and ecosystem viability. This research aims to assess the degree of water pollution, dissect and understand the myriad factors contributing to it, and pave the way for formul...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2024/28/shsconf_dsm2024_02016.pdf |
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| _version_ | 1850062497121304576 |
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| author | Shi Ruoyan |
| author_facet | Shi Ruoyan |
| author_sort | Shi Ruoyan |
| collection | DOAJ |
| description | Water pollution is a pivotal challenge, underpinning urgent conversations around environmental sustainability, public health, and ecosystem viability. This research aims to assess the degree of water pollution, dissect and understand the myriad factors contributing to it, and pave the way for formulating effective mitigation strategies and policies to preserve the integrity of water bodies worldwide. It highlights that rapid industrialization, population growth, and agriculture cause pollution. Industrial activities release pollutants like heavy metals, while agriculture contributes through runoff. Urbanization also exacerbates the problem. The study uses a dataset from Kaggle and selects variables like aluminium, ammonia, etc. A multiple logistic regression model analyses factors affecting water potability. Results show that aluminium, chloramine, and ammonia positively correlate with potability, while uranium and barium have negative ones. Interaction terms added to the model improve its fit. The study emphasizes understanding individual contaminants and their interactions for effective water management strategies. Accounting for these interactions enables a more comprehensive understanding of the factors affecting water safety. These insights are crucial for developing targeted and effective water management strategies that ensure safe drinking water and support public health. |
| format | Article |
| id | doaj-art-6ed98826414c4cd79876f489bb808cd9 |
| institution | DOAJ |
| issn | 2261-2424 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj-art-6ed98826414c4cd79876f489bb808cd92025-08-20T02:49:53ZengEDP SciencesSHS Web of Conferences2261-24242024-01-012080201610.1051/shsconf/202420802016shsconf_dsm2024_02016Research on evaluating water pollution determinants using multiple logistic regressionShi Ruoyan0Faulty of Arts and Science, University of TorontoWater pollution is a pivotal challenge, underpinning urgent conversations around environmental sustainability, public health, and ecosystem viability. This research aims to assess the degree of water pollution, dissect and understand the myriad factors contributing to it, and pave the way for formulating effective mitigation strategies and policies to preserve the integrity of water bodies worldwide. It highlights that rapid industrialization, population growth, and agriculture cause pollution. Industrial activities release pollutants like heavy metals, while agriculture contributes through runoff. Urbanization also exacerbates the problem. The study uses a dataset from Kaggle and selects variables like aluminium, ammonia, etc. A multiple logistic regression model analyses factors affecting water potability. Results show that aluminium, chloramine, and ammonia positively correlate with potability, while uranium and barium have negative ones. Interaction terms added to the model improve its fit. The study emphasizes understanding individual contaminants and their interactions for effective water management strategies. Accounting for these interactions enables a more comprehensive understanding of the factors affecting water safety. These insights are crucial for developing targeted and effective water management strategies that ensure safe drinking water and support public health.https://www.shs-conferences.org/articles/shsconf/pdf/2024/28/shsconf_dsm2024_02016.pdf |
| spellingShingle | Shi Ruoyan Research on evaluating water pollution determinants using multiple logistic regression SHS Web of Conferences |
| title | Research on evaluating water pollution determinants using multiple logistic regression |
| title_full | Research on evaluating water pollution determinants using multiple logistic regression |
| title_fullStr | Research on evaluating water pollution determinants using multiple logistic regression |
| title_full_unstemmed | Research on evaluating water pollution determinants using multiple logistic regression |
| title_short | Research on evaluating water pollution determinants using multiple logistic regression |
| title_sort | research on evaluating water pollution determinants using multiple logistic regression |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2024/28/shsconf_dsm2024_02016.pdf |
| work_keys_str_mv | AT shiruoyan researchonevaluatingwaterpollutiondeterminantsusingmultiplelogisticregression |