A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States
Abstract The correlation between poverty and air pollution in the contiguous United States is a widely debated issue, often suggesting that impoverished areas suffer higher pollution levels due to socioeconomic disparities. However, existing studies frequently lack the integration of advanced analyt...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87150-0 |
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| author | Sajeev Magesh Kevin Geng |
| author_facet | Sajeev Magesh Kevin Geng |
| author_sort | Sajeev Magesh |
| collection | DOAJ |
| description | Abstract The correlation between poverty and air pollution in the contiguous United States is a widely debated issue, often suggesting that impoverished areas suffer higher pollution levels due to socioeconomic disparities. However, existing studies frequently lack the integration of advanced analytical techniques and fail to account for a comprehensive range of variables. This research paper addresses these gaps by employing sophisticated machine learning models to analyze an extensive dataset encompassing various socioeconomic and environmental metrics. Utilizing linear regression, decision trees, and neural networks, we rigorously examined the data. While the variables were able to predict county-wide poverty rates to reasonably low RMSE and MAE values, our results indicate no significant correlation between poverty levels alone with air pollution indices. The latter finding challenges conventional understanding and highlights the complexity of the relationship between socioeconomic status and environmental quality. By offering a data-driven perspective, our study encourages policymakers to reconsider the factors influencing environmental justice and to look beyond economic status alone. Our work underscores the necessity for further investigation into other potential determinants of pollution, contributing to the discourse on environmental equity. This research provides a fresh, nuanced view that questions established beliefs and underscores the multifaceted nature of socioeconomic-environmental interactions. |
| format | Article |
| id | doaj-art-c78c61847cd64825a0f9b477355e40c4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c78c61847cd64825a0f9b477355e40c42025-01-19T12:18:28ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-025-87150-0A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United StatesSajeev Magesh0Kevin Geng1Dublin High SchoolDublin High SchoolAbstract The correlation between poverty and air pollution in the contiguous United States is a widely debated issue, often suggesting that impoverished areas suffer higher pollution levels due to socioeconomic disparities. However, existing studies frequently lack the integration of advanced analytical techniques and fail to account for a comprehensive range of variables. This research paper addresses these gaps by employing sophisticated machine learning models to analyze an extensive dataset encompassing various socioeconomic and environmental metrics. Utilizing linear regression, decision trees, and neural networks, we rigorously examined the data. While the variables were able to predict county-wide poverty rates to reasonably low RMSE and MAE values, our results indicate no significant correlation between poverty levels alone with air pollution indices. The latter finding challenges conventional understanding and highlights the complexity of the relationship between socioeconomic status and environmental quality. By offering a data-driven perspective, our study encourages policymakers to reconsider the factors influencing environmental justice and to look beyond economic status alone. Our work underscores the necessity for further investigation into other potential determinants of pollution, contributing to the discourse on environmental equity. This research provides a fresh, nuanced view that questions established beliefs and underscores the multifaceted nature of socioeconomic-environmental interactions.https://doi.org/10.1038/s41598-025-87150-0Air pollutionEnvironmental justiceSocioeconomic statusDemographicsMachine learningUnited States |
| spellingShingle | Sajeev Magesh Kevin Geng A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States Scientific Reports Air pollution Environmental justice Socioeconomic status Demographics Machine learning United States |
| title | A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States |
| title_full | A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States |
| title_fullStr | A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States |
| title_full_unstemmed | A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States |
| title_short | A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States |
| title_sort | machine learning interpretation of the correlation between poverty and air pollution in the contiguous united states |
| topic | Air pollution Environmental justice Socioeconomic status Demographics Machine learning United States |
| url | https://doi.org/10.1038/s41598-025-87150-0 |
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