research-article

Forecasting air pollution load in Delhi using data analysis tools

Authors:

Vaishali Sagar,

Arshita BhattAuthors Info & Claims

Volume 132, Issue C

Pages 1077 - 1085

https://doi.org/10.1016/j.procs.2018.05.023

Published: 01 January 2018 Publication History

Abstract

The enormity of air pollution has always been a matter of concern due to rapid development and urbanization over a long period. The increasing level of pollutants in ambient air in 2016-2017 has deteriorated the air quality of Delhi at an alarming rate. This brought us to focus our study on air quality in Delhi region. The prediction of future air quality has been carried out by analyzing the pollutants using data analysis techniques.

In our previous study, we had analyzed the data from 2011-2015. Detailed analysis from 2009-2017 of air pollutants has been proposed in this extended paper along with the critical observation of 2016-2017 air pollutants trend in Delhi. Descriptive analysis and predictive analysis have been used to study the trends of various air pollutants like sulphur dioxide (SO₂), nitrogen dioxide (NO₂), suspended particulate matter (PM), ozone (O₃) carbon monoxide (CO), benzene, and forecast the future trend. We have observed through data analytics techniques that SO₂ is likely to increase by 1.24ug/m³, NO₂ is likely to increase by 16.77ug/m³, O3 is likely to increase by 6.11 mg/m³, benzene is likely to reduce by 1.33 mg/m³ and NO₂ is predicted to reduce by 0.169 mg/m³ in the coming years.

References

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Cited By

Zeng BZheng TXu XWang J(2024)A novel structure grey prediction model with strong compatibility and its application in forecasting the annual average concentration of particulate matter in BeijingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108974136:PAOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108974

Index Terms

Forecasting air pollution load in Delhi using data analysis tools
1. Applied computing
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Procedia Computer Science

Procedia Computer Science Volume 132, Issue C

2018

1876 pages

ISSN:1877-0509

EISSN:1877-0509

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Copyright © 2018.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2018

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Cited By

Zeng BZheng TXu XWang J(2024)A novel structure grey prediction model with strong compatibility and its application in forecasting the annual average concentration of particulate matter in BeijingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108974136:PAOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108974

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