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An Analytical Study to Find the Major Factors Behind the Great Smog of Delhi, 2016: Using Fundamental Data Sciences

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Data Science and Analytics (REDSET 2017)

Abstract

Concerns over the alarming situation of Smog Pollution have come under the broad and current interests of masses since past few decades. Exposure to augmented levels of pollutants forming Photochemical Smog poses threat to human life, plants, animals and property as well. The effects of Smog on health can be felt instantaneously, ranging from minor pains to deadly pulmonary diseases such as lung cancer. The Great Smog of Delhi, 2016 was a manifestation of such situations. The air quality dipped to hazardous levels posing a health emergency situation. With an aim to know the intricacies of the problem, an Analytical study of the factors that contributed to Smog Pollution in Delhi was carried out using fundamental Data Sciences in R programming language. The study covers the major Pollutant analysis and Meteorological factors from the data of two pollution monitoring stations viz. R.K. Puram and Mandir Marg. Statistical Analysis and simple Linear Regression models were used for the correlation study between pollutants’ concentration levels and meteorological factors. Also, the comparative analysis was executed over the conditions of monitoring stations under study. It was found that the Particulate Matter (PM10) turned up as the major pollutant. Concentrations of pollutants are also affected by the meteorological factors. Less greenery and exposure to more vehicular pollution resulted in R.K. Puram being more polluted than Mandir Marg.

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Notes

  1. 1.

    All the empirical data sets and the experimental works in the form of the code in R language or the ‘R Scripts’, and the plots generated by the codes are available at our GITHUB repository: https://github.com/A-Infinite/DataScience-DelhiSmog2016Analysis.

References

  1. Soumya Pillai on November 8, 2016: Delhi’s pollution levels worse than the great London smog of 1952: EPA. http://www.hindustantimes.com/delhi-news/delhi-s-pollution-levels-worse-than-the-great-london-smog-of-1952-epa/story-Wv54jr5rMSAojqRZ85zdWO.html

  2. India Today Article by Prabahsh K. Dutta, October 29, 2016. http://indiatoday.intoday.in/story/haze-descends-in-delhi-ahead-of-diwali-air-hazardous/1/798553.html

  3. The Wall Street Journal of November 1, 2106 By Karan Deep Singh. https://blogs.wsj.com/indiarealtime/2016/11/01/air-pollution-in-new-delhi-gets-dangerously-high-during-diwali-celebrations/

  4. Data Source: Central Pollution Control Board. http://www.cpcb.gov.in/CAAQM/frmUserAvgReportCriteria.aspx

  5. Comprehensive Environmental Assessment of Industrial Clusters by J. S. Kamyotra, Member Secretary, Central Pollution Control Board, Delhi – 110 032

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  6. At ENVIS Centre – 01. cpcb.nic.in/divisionsofheadoffice/ess/NewItem_152_Final-Book_2.pdf

  7. Correlation Analysis from DjsResearch website. Link. http://www.djsresearch.co.uk/glossary/item/correlation-analysis-market-research

  8. Influence of Meteorological Factors on Air Quality from Queensland Government Website. https://www.qld.gov.au/environment/pollution/monitoring/airmonitoring/meteorology-factors/

  9. Sulphur Dioxide: It’s Role in Climatic Change. http://esseacourses.strategies.org/module.php?module_id=168

  10. Hindustan Times, November 8, 2016. http://www.hindustantimes.com/delhi-news/all-you-wanted-to-know-about-delhi-air-pollution-cleared-up-here/story-V7EkSU7xtNdnlimBYxGg6K.html

  11. All the Empirical Data and the Experiments Work in the form of the code in R Language or the ‘R Scripts’, and the Plots Generated by the Codes are Available at our GITHUB Repository. https://github.com/A-Infinite/DataScience-DelhiSmog2016Analysis

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Correspondence to Arushi Bhatt .

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Sharma, D.K., Bhatt, A., Kumar, A. (2018). An Analytical Study to Find the Major Factors Behind the Great Smog of Delhi, 2016: Using Fundamental Data Sciences. In: Panda, B., Sharma, S., Roy, N. (eds) Data Science and Analytics. REDSET 2017. Communications in Computer and Information Science, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-10-8527-7_18

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  • DOI: https://doi.org/10.1007/978-981-10-8527-7_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8526-0

  • Online ISBN: 978-981-10-8527-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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