Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909654513090560 |
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| author | Wang, Shuangbao Paul Yang, Lucas Chouchane, Rahouane Guo, Jin Bailey, Michael |
| author_facet | Wang, Shuangbao Paul Yang, Lucas Chouchane, Rahouane Guo, Jin Bailey, Michael |
| contents | In this study, we apply machine learning and software engineering in analyzing air pollution levels in City of Baltimore. The data model was fed with three primary data sources: 1) a biased method of estimating insurance risk used by homeowners loan corporation, 2) demographics of Baltimore residents, and 3) census data estimate of NO2 and PM2.5 concentrations. The dataset covers 650,643 Baltimore residents in 44.7 million residents in 202 major cities in US. The results show that air pollution levels have a clear association with the biased insurance estimating method. Great disparities present in NO2 level between more desirable and low income blocks. Similar disparities exist in air pollution level between residents' ethnicity. As Baltimore population consists of a greater proportion of people of color, the finding reveals how decades old policies has continued to discriminate and affect quality of life of Baltimore citizens today. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact Wang, Shuangbao Paul Yang, Lucas Chouchane, Rahouane Guo, Jin Bailey, Michael Computers and Society Machine Learning In this study, we apply machine learning and software engineering in analyzing air pollution levels in City of Baltimore. The data model was fed with three primary data sources: 1) a biased method of estimating insurance risk used by homeowners loan corporation, 2) demographics of Baltimore residents, and 3) census data estimate of NO2 and PM2.5 concentrations. The dataset covers 650,643 Baltimore residents in 44.7 million residents in 202 major cities in US. The results show that air pollution levels have a clear association with the biased insurance estimating method. Great disparities present in NO2 level between more desirable and low income blocks. Similar disparities exist in air pollution level between residents' ethnicity. As Baltimore population consists of a greater proportion of people of color, the finding reveals how decades old policies has continued to discriminate and affect quality of life of Baltimore citizens today. |
| title | Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2506.17319 |