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Machine learning-based detection of sudden air pollutant level changes: impacts on public health

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Abstract

Pollution originating from human activities can harm both humans and ecosystems. Our research underscores the detrimental impact of human-induced pollution on both human health and ecosystems. Prolonged exposure to pollution poses health risks, but sudden surges in pollutant levels can be even more hazardous. We have devised a robust method to detect abrupt changes in air quality resulting from human activities. Our study involved real-time analysis of PM\(_{2.5}\) and PM\(_{10}\) pollution data collected over 18 months from an industrial-based suburban city. We meticulously analyzed spatial and meteorological patterns in pollutant concentrations using a grid-based system equipped with monitoring devices. Remarkably, our approach exhibited an impressive 97.83% accuracy in predicting sudden air quality changes. By comprehensively understanding pollution patterns, our research contributes significantly to the development of enhanced remediation techniques.

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Data availibility

In the device deployment section, the dataset link is provided.

Notes

  1. shorturl./BNP69.

  2. https://dnr.wisconsin.gov/topic/OpenBurning/Impacts.html.

  3. https://shorturl.at/fH368.

  4. https://github.com/Pritisha94/Pollution-Data.git.

  5. https://www.downtoearth.org.in/blog/national-air-quality-index-a-solution-with-too-many-problems-49465.

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Authors and Affiliations

  1. National Institute of Technology, Durgapur, India

    Pritisha Sarkar & Mousumi Saha

Authors
  1. Pritisha Sarkar
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  2. Mousumi Saha
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Correspondence to Pritisha Sarkar.

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Sarkar, P., Saha, M. Machine learning-based detection of sudden air pollutant level changes: impacts on public health. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01918-7

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