Air Quality, Atmosphere & Health

, Volume 12, Issue 3, pp 327–340 | Cite as

Health risk associated with potential source regions of PM2.5 in Indian cities

  • Shovan Kumar Sahu
  • Hongliang Zhang
  • Hao Guo
  • Jianlin Hu
  • Qi Ying
  • Sri Harsha KotaEmail author


This paper estimates the regional contribution of high PM2.5 concentration and associated mortality using HYSPLIT back trajectory analysis in eight Indian cities during 2015–2016. Health risk and mortality estimation were carried out using the Integrated Exposure Response function (IER) which was verified using our previous time series study in Delhi. Risk estimates from IER were observed to be slightly over-predicted (2.14%) when compared to health risk from time series study in Delhi. Health risk in the eight cities across the four seasons indicated higher chronic obstructive pulmonary disease (COPD), lung cancer (LC), ischemic heart disease (IHD), and stroke in the northern (COPD = 1.35, LC = 1.50, IHD = 1.39, Stroke = 2.06) and eastern cities (COPD = 1.27, LC = 1.38, IHD = 1.35, Stroke = 1.93) as compared to in southern or western cities. Risk of stroke was observed to be the highest: North = 1.37–1.52, South = 1.20–1.31, East = 1.40–1.52, and West = 1.24–1.35 times to that of other diseases. Uttar Pradesh was observed to be a major contributor to premature mortality in Delhi, Lucknow, and Patna accounting for 30, 71, and 42% of total premature death due to high PM2.5 concentration during winter. Similarly, high PM2.5 concentration from West Bengal and Bangladesh was responsible for 52% of total premature mortality in Kolkata while the Indian Ocean was a major contributor to premature mortality in western and southern cities during winter. Reduction of both local and regional pollution is required to yield a significant reduction in pollution of all cities except Delhi and Lucknow where regional and local sources respectively are dominant.


HYSPLIT PM2.5 Source regions India Health risk Premature mortality 



The authors would like to thank the ministry of human resources and development, India and supercomputing facility in IITG.

Supplementary material

11869_2019_661_MOESM1_ESM.docx (3.1 mb)
ESM 1 (DOCX 3147 kb)


  1. Brauer M, Freedman G, Frostad J, van Donkelaar A, Martin RV, Dentener F, Dingenen Rv, Estep K, Amini H, Apte JS, Balakrishnan K, Barregard L, Broday D, Feigin V, Ghosh S, Hopke PK, Knibbs LD, Kokubo Y, Liu Y, Ma S, Morawska L, Sangrador JLT, Shaddick G, Anderson HR, Vos T, Forouzanfar MH, Burnett RT, Cohen A (2016) Ambient air pollution exposure estimation for the global burden of disease 2013. Environ Sci Technol 50:79–88Google Scholar
  2. Burnett RT, Pope CA 3rd, Ezzati M, Olives C, Lim SS, Mehta S, Shin HH, Singh G, Hubbell B, Brauer M, Anderson HR, Smith KR, Balmes JR, Bruce NG, Kan H, Laden F, Pruss-Ustun A, Turner MC, Gapstur SM, Diver WR, Cohen A (2014) An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ Health Perspect 122:397–403CrossRefGoogle Scholar
  3. Cairncross EK, John J, Zunckel M (2007) A novel air pollution index based on the relative risk of daily mortality associated with short-term exposure to common air pollutants. Atmos Environ 41:8442–8454CrossRefGoogle Scholar
  4. Carslaw DC, Ropkins K (2012) Openair—an R package for air quality data analysis. Environ Model Softw 27–28:52–61CrossRefGoogle Scholar
  5. Chakraborty A, Gupta T (2010) Chemical characterization and source apportionment of submicron (PM1) aerosol in Kanpur region, India. Aerosol Air Qual Res 10:433–445CrossRefGoogle Scholar
  6. Cheng I, Zhang L, Blanchard P, Dalziel J, Tordon R (2013) Concentration-weighted trajectory approach to identifying potential sources of speciated atmospheric mercury at an urban coastal site in Nova Scotia, Canada. Atmos Chem Phys 13:6031–6048CrossRefGoogle Scholar
  7. Chowdhury S, Dey S (2016) Cause-specific premature death from ambient PM2.5 exposure in India: estimate adjusted for baseline mortality. Environ Int 91:283–290CrossRefGoogle Scholar
  8. CPCB (2011). Guidelines for the measurement of ambient air pollutants, CPCBGoogle Scholar
  9. Csavina J, Field J, Félix O, Corral-Avitia AY, Sáez AE, Betterton EA (2014) Effect of wind speed and relative humidity on atmospheric dust concentrations in semi-arid climates. Sci Total Environ 487:82–90CrossRefGoogle Scholar
  10. Draxier RR, Hess GD (1998) An overview of the HYSPLIT_4 modelling system for trajectories, dispersion and deposition. Aust Meteorol Mag 47:295–308Google Scholar
  11. Elminir HK (2005) Dependence of urban air pollutants on meteorology. Sci Total Environ 350:225–237CrossRefGoogle Scholar
  12. Franklin M, Zeka A, Schwartz J (2006) Association between PM2.5 and all-cause and specific-cause mortality in 27 US communities. J Expo Sci Environ Epidemiol 17:279–287Google Scholar
  13. Greenpeace (2016). Deaths Due to Outdoor Air Pollution in India and China, In Clean Air Nation.Google Scholar
  14. Ghosh S, Biswas J, Guttikunda S, Roychowdhury S, Nayak M (2015) An investigation of potential regional and local source regions affecting fine particulate matter concentrations in Delhi, India. J Air Waste Manage Assoc 65:218–231CrossRefGoogle Scholar
  15. Gogikar P, Tyagi B (2016) Assessment of particulate matter variation during 2011-2015 over a Tropical Station Agra, India. Atmos Environ 147:11–21CrossRefGoogle Scholar
  16. Gogoi MM, Krishna Moorthy K, Babu SS and Bhuyan PK (2009) Climatology of columnar aerosol properties and the influence of synoptic conditions: first-time results from the northeastern region of India. J Geophys Res: Atmos 114: n/a-n/aGoogle Scholar
  17. Guttikunda SK, Jawahar P (2012) Application of SIM-air modeling tools to assess air quality in Indian cities. Atmos Environ 62:551–561CrossRefGoogle Scholar
  18. Guttikunda SK, Jawahar P (2014) Characterizing Patna’s ambient air quality and assessing opportunities for policy intervention, In UrbanEmissions.Info, New Delhi, IndiaGoogle Scholar
  19. Guttikunda SK, Kopakka RV (2014) Source emissions and health impacts of urban air pollution in Hyderabad, India. Air Qual Atmos Health 7:195–207CrossRefGoogle Scholar
  20. Guttikunda SK, Goel R, Pant P (2014) Nature of air pollution, emission sources, and management in the Indian Cities. Atmos Environ 95:501–510CrossRefGoogle Scholar
  21. He H, Vinnikov KY, Li C, Krotkov NA, Jongeward AR, Li Z, Stehr JW, Hains JC, Dickerson RR (2016) Response of SO2 and particulate air pollution to local and regional emission controls: a case study in Maryland. Earth’s Future 4:94–109Google Scholar
  22. Holzworth GC (1972) Mixing heights, wind speeds, and potential for urban air pollution throughout the Contiguous United States, In Epa Publication, EPAGoogle Scholar
  23. Kalluri ROR, Gugamsetty B, Kotalo RG, Nagireddy SKR, Tandule CR, Thotli LR, Shaik NH, Maraka VR, Rajuru RR, Surendran Nair SB (2017) Seasonal variation of near surface black carbon and satellite derived vertical distribution of aerosols over a semi-arid station in India. Atmos Res 184:77–87CrossRefGoogle Scholar
  24. Kota SH, Guo H, Myllyvirta L, Hu J, Sahu SK, Garaga R, Ying Q, Gao A, Dahiya S, Wang Y (2018) Year-long simulation of gaseous and particulate air pollutants in India. Atmos Environ 180:244–255CrossRefGoogle Scholar
  25. Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi Y, Turner MC, Pope CA III, Thurston G, Calle EE (2009) Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. Research report (Health Effects Institute) 140:5–114Google Scholar
  26. Kulshrestha A, Satsangi PG, Masih J, Taneja A (2009) Metal concentration of PM2.5 and PM10 particles and seasonal variations in urban and rural environment of Agra, India. Sci Total Environ 407:6196–6204CrossRefGoogle Scholar
  27. Lelieveld J, Crutzen PJ, Ramanathan V, Andreae MO, Brenninkmeijer CAM, Campos T, Cass GR, Dickerson RR, Fischer H, de Gouw JA, Hansel A, Jefferson A, Kley D, de Laat ATJ, Lal S, Lawrence MG, Lobert JM, Mayol-Bracero OL, Mitra AP, Novakov T, Oltmans SJ, Prather KA, Reiner T, Rodhe H, Scheeren HA, Sikka D, Williams J (2001) The Indian Ocean experiment: widespread air pollution from South and Southeast Asia. Science 291:1031–1036CrossRefGoogle Scholar
  28. Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A (2015) The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525:367–371CrossRefGoogle Scholar
  29. Madrid L, Diaz-Barrientos E, Ruiz-Cortés E, Reinoso R, Biasioli M, Davidson CM, Duarte A, Grčman H, Hossack I, Hursthouse AS (2006) Variability in concentrations of potentially toxic elements in urban parks from six European cities. J Environ Monit 8:1158–1165CrossRefGoogle Scholar
  30. Müller J (1982) Residence time and deposition of particle-bound atmospheric substances, In Deposition of atmospheric pollutants, Springer, pp. 43–52Google Scholar
  31. NEERI (2010) Air quality monitoring, emission inventory & source apportionment studies for MumbaiGoogle Scholar
  32. Ram K, Sarin M, Tripathi S (2012) Temporal trends in atmospheric PM2. 5, PM10, elemental carbon, organic carbon, water-soluble organic carbon, and optical properties: impact of biomass burning emissions in the Indo-Gangetic Plain. Environ Sci Technol 46:686–695Google Scholar
  33. Sahu SK, Kota SH (2017) Significance of PM2.5 air quality at the Indian capital. Aerosol Air Qual Res 17:588–597CrossRefGoogle Scholar
  34. Sancho J, Martínez J, Pastor JJ, Taboada J, Piñeiro JI, García-Nieto PJ (2014) New methodology to determine air quality in urban areas based on runs rules for functional data. Atmos Environ 83:185–192CrossRefGoogle Scholar
  35. Sandeep P, Saradhi IV, Pandit GG (2013) Seasonal variation of black carbon in fine particulate matter (PM2.5) at the tropical coastal city of Mumbai, India. Bull Environ Contam Toxicol 91:605–610CrossRefGoogle Scholar
  36. Schäfer K, Emeis S, Hoffmann H, Jahn C (2006) Influence of mixing layer height upon air pollution in urban and sub-urban areas. Meteorol Z 15:647–658CrossRefGoogle Scholar
  37. Sen A, Abdelmaksoud A, Ahammed YN, Banerjee T, Bhat MA, Chatterjee A, Choudhuri AK, Das T, Dhir A, Dhyani PP (2017) Variations in particulate matter over Indo-Gangetic Plains and Indo-Himalayan range during four field campaigns in winter monsoon and summer monsoon: role of pollution pathways. Atmos Environ 154:200–224CrossRefGoogle Scholar
  38. Shah JJ, Nagpal T (1997) Urban air quality management strategy in Asia: greater Mumbai report. World Bank PublicationsGoogle Scholar
  39. Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD, Ngan F (2015) NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc 96:2059–2077CrossRefGoogle Scholar
  40. Stohl A (1998) Computation, accuracy and applications of trajectories—a review and bibliography. Atmos Environ 32:947–966CrossRefGoogle Scholar
  41. Stohl A, Seibert P (1998) Accuracy of trajectories as determined from the conservation of meteorological tracers. Q J R Meteorol Soc 124:1465–1484CrossRefGoogle Scholar
  42. Stohl A, Eckhardt S, Forster C, James P, Spichtinger N, Seibert P (2002) A replacement for simple back trajectory calculations in the interpretation of atmospheric trace substance measurements. Atmos Environ 36:4635–4648CrossRefGoogle Scholar
  43. Su L, Yuan Z, Fung JCH, Lau AKH (2015) A comparison of HYSPLIT backward trajectories generated from two GDAS datasets. Sci Total Environ 506-507:527–537CrossRefGoogle Scholar
  44. Team RC (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  45. Tecer LH, Alagha O, Karaca F, Tuncel G, Eldes N (2008) Particulate matter (PM2.5, PM10–2.5, and PM10) and children’s hospital admissions for asthma and respiratory diseases: a bidirectional case-crossover study. J Toxic Environ Health A 71:512–520CrossRefGoogle Scholar
  46. Uria-Tellaetxe I, Carslaw DC (2014) Conditional bivariate probability function for source identification. Environ Model Softw 59:1–9CrossRefGoogle Scholar
  47. Velmurugan T, Santhanam T (2010) Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points. J Comput Sci 6:363–368CrossRefGoogle Scholar
  48. Villalobos AM, Amonov MO, Shafer MM, Devi JJ, Gupta T, Tripathi SN, Rana KS, McKenzie M, Bergin MH, Schauer JJ (2015) Source apportionment of carbonaceous fine particulate matter (PM2.5) in two contrasting cities across the Indo-Gangetic Plain. Atmos Pollut Res 6:398–405Google Scholar
  49. Wang J, Ogawa S (2015) Effects of meteorological conditions on PM(2.5) concentrations in Nagasaki, Japan. Int J Environ Res Public Health 12:9089–9101CrossRefGoogle Scholar
  50. WHO. WHO Global Urban Ambient Air Pollution Database (Update 2016),, Last Access: 11th,Feb.
  51. Ying Q, Wu L, Zhang H (2014) Local and inter-regional contributions to PM2.5 nitrate and sulfate in China. Atmos Environ 94:582–592CrossRefGoogle Scholar
  52. Zhang H, Hu J, Kleeman M, Ying Q (2014) Source apportionment of sulfate and nitrate particulate matter in the eastern United States and effectiveness of emission control programs. Sci Total Environ 490:171–181CrossRefGoogle Scholar
  53. Zhang H, Wang Y, Hu J, Ying Q, Hu X-M (2015) Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environ Res 140:242–254CrossRefGoogle Scholar

Copyright information

© Springer Media B.V., onderdeel van Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia
  2. 2.Department of Civil and Environmental EngineeringLouisiana State UniversityBaton RougeUSA
  3. 3.Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Centre of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science & TechnologyNanjingChina
  4. 4.Zachry Department of Civil EngineeringTexas A&M UniversityCollege StationUSA
  5. 5.Department of Civil EngineeringIndian Institute of Technology DelhiNew DelhiIndia

Personalised recommendations