Advertisement

Inferring air pollution from air quality index by different geographical areas: case study in India

  • Rohit Sharma
  • Raghvendra Kumar
  • Devendra Kumar Sharma
  • Le Hoang Son
  • Ishaani Priyadarshini
  • Binh Thai Pham
  • Dieu Tien BuiEmail author
  • Sakshi Rai
Article
  • 12 Downloads

Abstract

India is one of the most polluted countries in the world, where several major cities are facing serious environmental consequences as a result of rapid pollution growth. The objective of this research is to analyze air pollution trends with respect to various geographical locations, in order to have a global view of the damage caused, so that appropriate actions can be developed in the future to prevent air pollution. In this regard, the polluted database was established based on the data provided by the Central Pollution Control Board; Ministry of Environment, Forest, and Climate Change (India). These data demonstrate the annual growth of SO2, NOx, and particulate matter (PM) 2.5 from 2015 to 2018 and were recorded at various monitoring stations in three cities, namely, Delhi, Bengaluru, and Chennai. The results show that SO2, NOx, and PM 2.5 were from different transport modes, both small or large-scale power generations (from diesel, coal and gas plant), industries, constructions, and domestic cooking. Overall, there was an increasing trend, day by day, in India. The result categorized the considered areas into the following four classes: critically polluted (CP), highly polluted (HP), moderately polluted (MP), and low polluted (LP). The results will assist in the assessment of pollution for the cities investigated in this research.

Keywords

Air pollution Air pollutants Environmental health Exceedance factor Data analysis 

Notes

References

  1. Amal L, Son LH, Chabchoub H (2018) SGA: spatial GIS-based genetic algorithm for route optimization of municipal solid waste collection. Environ Sci Pollut Res 25:27569–27582.  https://doi.org/10.1007/s11356-018-2826-0 CrossRefGoogle Scholar
  2. Ayala A, Brauer M, Mauderly JL, Samet JM (2011) Air pollutants and sources associated with health effects. Air Qual Atmos Health 5:151–167.  https://doi.org/10.1007/s11869-011-0155-2 CrossRefGoogle Scholar
  3. Balakrishnan K, Cohen A, Smith KR (2014) Addressing the burden of disease attributable to air pollution in India: the need to integrate across household and ambient air pollution exposures. Environ Health Perspect 122:A6–A7.  https://doi.org/10.1289/ehp.1307822 CrossRefGoogle Scholar
  4. Biswas J, Upadhyay E, Nayak M, Yadav AK (2011) An analysis of ambient air quality conditions over Delhi, India from 2004 to 2009. Atmos Clim Sci 01:214–224.  https://doi.org/10.4236/acs.2011.14024 Google Scholar
  5. Camastra F, Ciaramella A, Son LH, Riccio A, Staiano A (2019) Fuzzy similarity-based hierarchical clustering for atmospheric pollutants prediction. Springer International Publishing.  https://doi.org/10.1007/978-3-030-12544-8_10
  6. Cardozo JIH, Sánchez DFP (2019) An experimental and numerical study of air pollution near unpaved roads. Air Qual Atmos Health 12:471–489.  https://doi.org/10.1007/s11869-019-00678-9 CrossRefGoogle Scholar
  7. Chinnaswamy AK, Galvez MCD, Balisane H, Nguyen QT, Naguib RNG, Trodd N, Marshall IM, Yaacob N, Santos GNC, Vallar EA, Shaker M, Wickramasinghe N, Ton TN (2016) Air pollution in Bangalore, India: an eight-year trend analysis. Int J Environ Technol Manag 19:177.  https://doi.org/10.1504/ijetm.2016.082233 CrossRefGoogle Scholar
  8. CPCB (2016) Annual Report 2015–16. http://cpcb.nic.in/annual-report.php
  9. Dales R, Burnett RT, Smith-Doiron M, Stieb DM, Brook JR (2004) Air pollution and sudden infant death syndrome. PEDIATRICS 113:e628–e631.  https://doi.org/10.1542/peds.113.6.e628 CrossRefGoogle Scholar
  10. Donkelaar A et al (2016) Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors environmental. Sci Technol 50:3762–3772.  https://doi.org/10.1021/acs.est.5b05833 CrossRefGoogle Scholar
  11. GBD MAPS Working Group (2018) Burden of disease attributable to major air pollution sources in India. Special Report 21. Boston: Health Effects Institute.Google Scholar
  12. Gordon T, Balakrishnan K, Dey S, Rajagopalan S, Thornburg J, Thurston G, Agrawal A, Collman G, Guleria R, Limaye S, Salvi S, Kilaru V, Nadadur S (2018) Air pollution health research priorities for India: perspectives of the Indo-U.S. Communities of Researchers. Environ Int 119:100–108.  https://doi.org/10.1016/j.envint.2018.06.013 CrossRefGoogle Scholar
  13. Guttikunda SK, Nishadh KA, Jawahar P (2019) Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Clim 27:124–141.  https://doi.org/10.1016/j.uclim.2018.11.005 CrossRefGoogle Scholar
  14. Hedley AJ, Wong C-M, Thach TQ, Ma S, Lam T-H, Anderson HR (2002) Cardiorespiratory and all-cause mortality after restrictions on sulphur content of fuel in Hong Kong: an intervention study. Lancet 360:1646–1652.  https://doi.org/10.1016/s0140-6736(02)11612-6 CrossRefGoogle Scholar
  15. Heinrich J (2018) Air pollutants and primary allergy prevention. Allergo J Int 28:5–15.  https://doi.org/10.1007/s40629-018-0078-7 CrossRefGoogle Scholar
  16. Hoque MMM, Basak LK, Rokanuzzaman M, Roy S (2014) Level of noise pollution at different locations in Tangail municipal area, Bangladesh. Bangladesh J Sci Res 26:29–36.  https://doi.org/10.3329/bjsr.v26i1-2.20228 CrossRefGoogle Scholar
  17. Khazaei B, Shiehbeigi A, Haji Molla Ali Kani AR (2018) Modeling indoor air carbon dioxide concentration using artificial neural network. Int J Environ Sci Technol 16:729–736.  https://doi.org/10.1007/s13762-018-1642-x CrossRefGoogle Scholar
  18. Kim K-H, Kabir E, Kabir S (2015) A review on the human health impact of airborne particulate matter. Environ Int 74:136–143.  https://doi.org/10.1016/j.envint.2014.10.005 CrossRefGoogle Scholar
  19. Kumar A, Goyal P (2011a) Forecasting of air quality in Delhi using principal component regression technique. Atmos Pollut Res 2:436–444.  https://doi.org/10.5094/apr.2011.050 CrossRefGoogle Scholar
  20. Kumar A, Goyal P (2011b) Forecasting of daily air quality index in Delhi. Sci Total Environ 409:5517–5523.  https://doi.org/10.1016/j.scitotenv.2011.08.069 CrossRefGoogle Scholar
  21. Kumar A, Goyal P (2012) Forecasting of air quality index in Delhi using neural network based on principal component analysis. Pure Appl Geophys 170:711–722.  https://doi.org/10.1007/s00024-012-0583-4 CrossRefGoogle Scholar
  22. Limaye S, Salvi S (2010) Ambient air pollution and the lungs: what do clinicians need to know? Breathe 6:234–244Google Scholar
  23. Louati A, Son LH, Chabchoub H (2018) Smart routing for municipal solid waste collection: a heuristic approach. J Ambient Intell Humaniz Comput 10:1865–1884.  https://doi.org/10.1007/s12652-018-0778-3 CrossRefGoogle Scholar
  24. Pant P, Lal RM, Guttikunda SK, Russell AG, Nagpure AS, Ramaswami A, Peltier RE (2018) Monitoring particulate matter in India: recent trends and future outlook. Air Qual Atmos Health 12:45–58.  https://doi.org/10.1007/s11869-018-0629-6 CrossRefGoogle Scholar
  25. Ramasamy R (2018) Assessment of comprehensive environmental pollution index of Kurichi industrial cluster, Coimbatore District, Tamil Nadu, India - a case study. J Ecol Eng 19:191–199.  https://doi.org/10.12911/22998993/78747 CrossRefGoogle Scholar
  26. Schwela D (2012) Urban air pollution in Asian cities. Routledge.  https://doi.org/10.4324/9781849773676
  27. Son LH, Louati A (2016) Modeling municipal solid waste collection: a generalized vehicle routing model with multiple transfer stations, gather sites and inhomogeneous vehicles in time windows. Waste Manag 52:34–49.  https://doi.org/10.1016/j.wasman.2016.03.041 CrossRefGoogle Scholar
  28. Wang Z, Feng J, Fu Q, Gao S, Chen X, Cheng J (2019) Quality control of online monitoring data of air pollutants using artificial neural networks. Air Qual Atmos Health:1–8.  https://doi.org/10.1007/s11869-019-00734-4
  29. WHO (2018) WHO global ambient air quality database (update 2018). World Health Organization, GenevaGoogle Scholar
  30. Yuda M (2019) Asian countries rush to fight toxic air pollution. Accessed from https://asia.nikkei.com/Economy/Asian-countries-rush-to-fight-toxic-air-pollution. Accessed 24 Jan 2019
  31. Zheng Y, Li S, Zou C, Ma X, Zhang G (2018) Analysis of PM2.5 concentrations in Heilongjiang Province associated with forest cover and other factors. J For Res 30:269–276.  https://doi.org/10.1007/s11676-018-0640-7 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Electronics & Communication EngineeringSRM Institute of Science and TechnologyGhaziabadIndia
  2. 2.Department of Computer Science and EngineeringLNCT CollegeBhopalIndia
  3. 3.VNU Information Technology InstituteVietnam National UniversityHanoiVietnam
  4. 4.University of DelawareNewarkUSA
  5. 5.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  6. 6.Geographic Information Science Research GroupTon Duc Thang UniversityHo Chi Minh CityVietnam
  7. 7.Faculty of Environment and Labour SafetyTon Duc Thang UniversityHo Chi Minh CityVietnam
  8. 8.Department of Computer Science and EngineeringLNCT UniversityBhopalIndia

Personalised recommendations