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Predictive Analysis of Air Pollution Using Machine Learning Techniques

Predictive Analysis of Air Pollution Using Machine Learning Techniques Air pollution is a major source of worry for all living things. India has one of the world’s highest levels of air pollution. Rising population, unplanned growth, increased automotive traffic, stubble burning, industrial waste, fossil fuel combustion, powerplant emissions and a variety of other causes all contribute considerably to air pollution in developing countries. Particulate matter (PM) 2.5 is the most concerning of all air pollutants since it causes major health problems in individuals. Prediction and management of air quality have therefore become critical. Several machine learning algorithms were used in this work to examine dataset results. The results of our work suggest that for future predictions, logistic regression and autoregression can be efficaciously utilised for the analysis and forecasting of levels of PM2.5 in the future. Countries can lower the prevalence of strokes, and chronic and acute respiratory illnesses such as asthma, and lung cancer by reducing air pollution levels. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asia-Pacific Journal of Management Research and Innovation SAGE

Predictive Analysis of Air Pollution Using Machine Learning Techniques

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Publisher
SAGE
Copyright
© 2023 Asia-Pacific Institute of Management
ISSN
2319-510X
eISSN
2321-0729
DOI
10.1177/2319510x231155038
Publisher site
See Article on Publisher Site

Abstract

Air pollution is a major source of worry for all living things. India has one of the world’s highest levels of air pollution. Rising population, unplanned growth, increased automotive traffic, stubble burning, industrial waste, fossil fuel combustion, powerplant emissions and a variety of other causes all contribute considerably to air pollution in developing countries. Particulate matter (PM) 2.5 is the most concerning of all air pollutants since it causes major health problems in individuals. Prediction and management of air quality have therefore become critical. Several machine learning algorithms were used in this work to examine dataset results. The results of our work suggest that for future predictions, logistic regression and autoregression can be efficaciously utilised for the analysis and forecasting of levels of PM2.5 in the future. Countries can lower the prevalence of strokes, and chronic and acute respiratory illnesses such as asthma, and lung cancer by reducing air pollution levels.

Journal

Asia-Pacific Journal of Management Research and Innovation – SAGE

Published: Sep 1, 2022

Keywords: Pollution; linear regression; machine learning; air quality index (AQI)

References

  • Particulate matter components, sources, and health: Systematic approaches to testing effects
    Adams, K.; Greenbaum, D. S.; Shaikh, R.; van Erp, A. M.; Russell, A. G.
  • Supervised machine learning techniques in the desulfurization of oil products for environmental protection: A review
    Al-Jamimi, H. A.; Al-Azani, S.; Saleh, T. A.
  • Pollution and infant health
    Currie, J.
  • India has 9 Of World’s 10 most-polluted cities, but few air quality monitors
    Deshpande, T.
  • Hyperlocal air quality prediction using machine learning
    Gopalakrishnan, V.
  • Air pollution in India: Major issues and challenges
    Gurjar, B. R.
  • Air pollution prediction with machine learning: A case study of Indian cities
    Kumar, K.; Pande, B. P.
  • Air pollution prediction using machine learning supervised learning approach
    Madhuri, V. M.; Samyama, G. G. H.; Kamalapurkar, S.
  • Prediction and analysis of air quality using machine learning
    Monisri, P. R.; Vikas, R. K.; Rohit, N. K.; Varma, M. C.; Chaithanya, B. N.
  • Air quality index using machine learning: A Jordan case study
    Nahar, K.; Ottom, M. A.; Alshibli, F.; Shquier, M. A.
  • Pollution’s impact on historical monuments pollution’s impact on historical monuments
    Rogers, C. D.
  • Assessing the COVID-19 impact on air quality: A machine learning approach
    Rybarczyk, Y.; Zalakeviciute, R.
  • Implementation of machine learning algorithms for analysis and prediction of air quality
    Sanjeev, D.
  • The impact of PM2.5 on the human respiratory system
    Xing, Y.-F.; Xu, Y.-H.; Shi, M.-H.; Lian, Y.-X.