Earth Systems and Environment

, Volume 2, Issue 2, pp 305–322 | Cite as

Particulate Matter Assessment Using In Situ Observations from 2009 to 2014 over an Industrial Region of Eastern India

  • Priyanjali Gogikar
  • Bhishma TyagiEmail author
  • Rashmi Rekha Padhan
  • M. Mahaling
Original Article


The present study discusses the ambient air quality of an East Indian industrial region. The 8 hourly average concentrations of suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM) for the period January 2009–December 2014 was analyzed at two industrial sites: Rourkela Township and Rajgangpur; and a residential site in the vicinity of industrial sites, i.e., Sonaparbat situated in Sundergarh District of Orissa State, India. The study area holds one of the biggest steel plants in India, cement factory and many medium- and small-scale industries in its surrounding area. To understand the contribution of the fine mode (PM2.5—also called RSPM) and inhalable coarse particles (PM10also called SPM) to the particulate matter pollution, the ratio of PM2.5/PM10 is considered over the industrial and residential sites. Sonaparbat is loaded with more PM10 (particles concentration > 250 µg/m3) and dominance of PM2.5 was noticed during the years 2013 and 2014 compared to Rourkela and Rajgangpur. To detect the presence of specific emission sources that enhance the pollution over receptor sites, the conditional probability function and conditional bivariate probability function techniques are employed in the present study. Concentration weighted trajectory analysis using the 2-day back trajectory (by HYSPLIT-4 model) is also employed in the present study to discover the impact of transboundary pollution. Calm and weak wind speeds (< 1.5 ms−1) are noticed over the study area, thereby indicating the pollution due to local sources present in and around the city. Rourkela Steel Plant, Orissa Cements Limited (OCL), OCL Iron and Steel along with vehicular exhaust are some of the major local sources situated within the vicinity of 5 km in the study area. The results show that pollution levels have a significant contribution from adjacent industrial areas apart from local emission sources, especially in the northwesterly and southeasterly directions. Further, an attempt has been made to investigate the dispersion of pollutants from the study site to the nearby regions during the study period by employing the 48-h seasonal forward trajectory analysis using the HYSPLIT-4 model.


Particulate matter Source apportionment Conditional bivariate probability function Concentration weighted trajectory analysis HYSPLIT 4 



Ms. Priyanjali Gogikar would like to acknowledge the National Institute of Technology Rourkela for providing fellowship for conducting research. The authors acknowledge the Indian Space Research Organization (ISRO) for providing the wind data through MOSDAC, NCEP/NCAR for supplying GDAS 1° data and NOAA for the HYPLIT4 model. The authors also want to acknowledge the pollution control board team members, who showed their dedication in collecting these observations.

Compliance with Ethical Standards

Conflict of interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.


  1. Akinlade GO, Olaniyi HB, Olise FS, Owoade OK, Almeida SM, Almeida-Silva M, Hopke PK (2015) Spatial and temporal variations of the particulate size distribution and chemical composition over Ibadan, Nigeria. Environ Monit Assess 187(8):544CrossRefGoogle Scholar
  2. Begum BA, Kim E, Jeong CH, Lee DH, Hopke PK (2005) Evaluation of the potential source contribution function using the 2002 Quebec forest fire episode. Atmos Environ 39:3719–3724CrossRefGoogle Scholar
  3. Briggs NL, Long CM (2016) Critical review of black carbon and elemental carbon source apportionment in Europe and the United States. Atmos Environ 144:409–427CrossRefGoogle Scholar
  4. Census Report (2011) The Registrar General & Census Commissioner, India (
  5. Cheng I, Zhang L, Balanchard P, Dalzei J, Tordan R (2013) Concentration weighted trajectory approach to identifying potential sources of speciated atmospheric mercury at an urban coastal site in Nova Scotia, Canada. Amos Chem Phys 13:6031–6048Google Scholar
  6. Cheng I, Zhang L, Xu X (2016) Impact of measurement uncertainties on receptor modeling of speciated atmospheric mercury. Sci Rep 6(20676):1–11Google Scholar
  7. CPCB Report (2009) National Ambient Air Quality Standards (NAAQS), Gazette Notifcation, New DelhiGoogle Scholar
  8. CPCB Report (2015) National Air Quality Index. Series: CUPS/82/2014-15, pp 58Google Scholar
  9. Das S, Mohanty UC, Tyagi A, Sikka DR, Joseph PV, Rathore LS, Habib A, Baidya S, Sonam K, Sarkar A (2014) The SAARC STORM - a coordinated field experiment on severe thunderstorm observations and regional modeling over the south Asian region. Bull Am Meteorol Soc 95(4):603–617CrossRefGoogle Scholar
  10. Das S, Sarkar A, Das MK, Rahman MM, Islam MN (2015) Composite characteristics of Nor’westers based on observations and simulations. Atmos Res 158:158–178CrossRefGoogle Scholar
  11. Dimitriou K (2015) The dependence of PM size distribution from meteorology and local-regional contributions, in Valencia (Spain)—a CWT approach. Aerosol Air Qual Res 15:1979–1989CrossRefGoogle Scholar
  12. Dominici F, Greenstone M, Sunstein CR (2014) Particulate matter matters. Science 344:257–259CrossRefGoogle Scholar
  13. Draxler RR (1999) HYSPLIT4 user’s guide. NOAA Tech. Memo. ERL ARL-230, NOAA Air Resources Laboratory, Silver Spring, MDGoogle Scholar
  14. Draxler RR, Hess GD (1997) Description of the HYSPLIT_4 modelling system. NOAA Tech. Memo. ERL ARL-224, NOAA Air Resources Laboratory, Silver Spring, MD, p 24Google Scholar
  15. Draxler RR, Hess GD (1998) An overview of the HYSPLIT_4 modeling system for trajectories, dispersion, and deposition. Aust Meteor Mag 47:295–308Google Scholar
  16. Garg A, Shukla PR, Bhattacharya S, Dadhwal VK (2001) Subregion (district) and sector level SO2 and NOx emissions for India: assessment of inventories and mitigation flexibility. Atmos Environ 35:703–713CrossRefGoogle Scholar
  17. Gogikar P, Tyagi B (2016) Assessment of particulate matter variation during 2011–2015 over a tropical station Agra, India. Atmo Environ 147:11–21CrossRefGoogle Scholar
  18. Grigoras G, Cuculeanu V, Ene G, Mocioaca G, Deneanu A (2012) Air pollution dispersion modeling in a polluted industrial area of complex terrain from Romania. Rom Rep Phys 64(1):173–186Google Scholar
  19. Guttikunda SK, Gurjar BR (2012) Role of meteorology in seasonality of air pollution in megacity Delhi, India. Environ Monit Assess 184:3199–3211CrossRefGoogle Scholar
  20. Health Effects Institute (HEI) (2018) State of global air 2018. Special Report. Health Effects Institute, BostonGoogle Scholar
  21. Hsu YK, Holsen TM, Hopke PK (2003) Comparison of hybrid receptor models to locate PCB sources in Chicago. Atmos Environ 37:545–562CrossRefGoogle Scholar
  22. Jayamurugan R, Kumaravel B, Palanivelraja S, Chockalingam MP (2013) Influence of temperature, relative humidity and seasonal variability on ambient air quality in a coastal urban area. Int J Atmos Sci. CrossRefGoogle Scholar
  23. Karagulian F, Belis CA, Dora CFC, Prüss-Ustün AM, Bonjour S, Adair-Rohani H, Amann M (2015) Contributions to cities’ ambient particulate matter (PM): a systematic review of local source contributions at global level. Atmos Environ 120:475–483CrossRefGoogle Scholar
  24. Karar K, Gupta AK, Kumar A (2006) Characterization and identification of the sources of chromium, zinc, lead, cadmium, nickel, manganese and iron in PM10 particulates at the two sites of Kolkata, India. Environ Monit Assess 120:347–360CrossRefGoogle Scholar
  25. Kavuri NC, Paul KK and Roy N (2013) Regression modeling of gaseous air pollutants and meteorological parameters in a steel city, Rourkela. India Res J Recent Sci 285–289Google Scholar
  26. Kotchenruther RA (2016) Source apportionment of PM2.5 at multiple Northwest US sites: assessing regional winter wood smoke impacts from residential wood combustion. Atmos Environ 142:210–219CrossRefGoogle Scholar
  27. Lakshmi DD, Murty PLN, Bhaskaran PK, Sahoo B, Kumar TS, Shenoi SSC, Srikanth AS (2017) Performance of WRF-ARW winds on computed storm surge using hydrodynamic model for Phailin and Hudhud cyclones. Ocean Eng 131:135–148CrossRefGoogle Scholar
  28. Li Z, Hopke PK, Husain L, Qureshi S, Dutkiewicz VA, Schwab JJ, Demerjian KL (2004) Sources of fine particle composition in New York city. Atmos Environ 38(38):6521–6529CrossRefGoogle Scholar
  29. Li Y, Chang M, Ding S, Wang S, Ni D, Hu H (2017) Monitoring and source apportionment of trace elements in PM 2.5: implications for local air quality management. J Environ Manage 196:16–25CrossRefGoogle Scholar
  30. Liu Q, Baumgartner J, Zhang Y, Schauer JJ (2016) Source apportionment of Beijing air pollution during a severe winter haze event and associated pro-inflammatory responses in lung epithelial cells. Atmos Environ 126:28–35CrossRefGoogle Scholar
  31. Masiol M, Hopke PK, Felton HD, Frank BP, Rattigan OV, Wurth MJ, LaDuke GH (2017) Source apportionment of PM2.5 chemically speciated mass and particle number concentrations in New York City. Atmos Environ 148:215–229CrossRefGoogle Scholar
  32. MSME Report Balaghat (2012) Micro, Small and Medium enterprises development institute Udyog Vihar, Ministry of MSME, Government of India, p 12Google Scholar
  33. MSME report Chandrapur (2012) Micro, Small and Medium enterprises development institute Nagpur, Ministry of MSME, Government of India, p 25Google Scholar
  34. MSME Report Gondia (2012) Micro, Small and Medium enterprises development institute Nagpur, Ministry of MSME, Government of India, p 23Google Scholar
  35. MSME Report Lohardaga (2012) Micro, Small and Medium enterprises development institute Ranchi, Ministry of MSME, Government of India, p 13Google Scholar
  36. MSME Report Nagpur (2012) Micro, Small and Medium enterprises development institute Nagpur, Ministry of MSME, Government of India, p 18Google Scholar
  37. MSME Report Sidhi (2012) Micro, Small and Medium enterprises development institute Udyog Vihar, Ministry of MSME, Government of India, p 16Google Scholar
  38. MSME Report Singrauli (2016) Micro, Small and Medium enterprises development institute Indore, Ministry of MSME, Government of India, p 13Google Scholar
  39. Nel A (2005) Air pollution-related illness: effects of particles. Science 308:804–806CrossRefGoogle Scholar
  40. Pekney NJ, Davidson CI, Robinson A, Zhou L, Hopke PK, Eatough D, Rogge WF (2006) Major source categories for PM2.5 in Pittsburgh using PMF and UNMIX. Aerosol Sci Technol 40:910–924CrossRefGoogle Scholar
  41. Querol X, Alastuey A, Ruiz CR, Artiñano B, Hansson HC, Harrison RM, Straehl P (2004) Speciation and origin of PM10 and PM2.5 in selected European cities. Atmos Environ 38(38):6547–6555CrossRefGoogle Scholar
  42. Rai P, Chakraborty A, Mandariya AK, Gupta T (2016) Composition and source apportionment of PM1 at urban site Kanpur in India using PMF coupled with CBPF. Atmos Res 178(179):506–520CrossRefGoogle Scholar
  43. Rao MN, Rao HVN (1989) Air pollution indices: air pollution. Tata McGraw-Hill Publishing Ltd, New Delhi, pp 271–272Google Scholar
  44. Ray K, Bandopadhyay BK, Sen B, Sharma P (2014) Thunderstorms 2014- A Report SAARC storm project 2014. IMD Report Number: ESSO/IMD/SMRC STORM Project-2014/01(2014)/03, India Meteorological Department, Ministry of Earth Sciences, Government of IndiaGoogle Scholar
  45. SAIL Annual Report (2016) Steel Authority of India Limited Annual report 2015-2016, p 164.
  46. Saraf AK, Bora AK, Das J, Rawat V, Sharma K, Jain SK (2010) Winter fog over the Indo-Gangetic plains mapping and modeling using remote sensing and GIS. Nat Hazards 58(1):199–220CrossRefGoogle Scholar
  47. Sarasamma JD, Narayanan BK (2014) Air quality assessment in the surroundings of KMML industrial area, Chavara in Kerala, South India. Aerosol Air Qual Res 14(6):1769–1778CrossRefGoogle Scholar
  48. Seibert P, Kromp-Kolb H, Baltensperger U, Jost DT, Schwikowski M (1994) Trajectory analysis of high-alpine air pollution data. In: Gryning SE, Millán MM (eds) Air pollution modeling and its application X. NATO. Challenges of modern society, vol 18. Springer, Boston, MACrossRefGoogle Scholar
  49. Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD, Ngan F (2015) NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull Amer Meteor Soc 96:2059–2077CrossRefGoogle Scholar
  50. 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):537Google Scholar
  51. Sugimoto N, Shimizu A, Matsui I, Nishikawa M (2016) A method for estimating the fraction of mineral dust in particulate matter using PM 2.5-to-PM 10 ratios. Particuology 28:114–120CrossRefGoogle Scholar
  52. Tiwari S, Srivastava AK, Bisht DS, Safai PD, Parmita P (2012) Assessment of carbonaceous aerosol over Delhi in the Indo-Gangetic Basin: characterization, sources, and temporal variability. Nat Hazards 65:1745–1764CrossRefGoogle Scholar
  53. Tiwari S, Dumka UC, Gautam AS, Kaskaoutis DG, Srivastava AK, Bisht DS, Chakrabarty RK, Sumlin BJ, Solmon F (2017) Assessment of over Guwahati in Brahmaputra River Valley: temporal evolution, source apportionment and meteorological dependence. Atmos Pol Res 8(1):13–28CrossRefGoogle Scholar
  54. Uria-Tellaetxe I, Carslaw DC (2014) Conditional bivariate probability function for source identification. Environ Model Soft 9:1–9CrossRefGoogle Scholar
  55. U.S. EPA (2004) Air quality criteria for particulate matter (Final Report, Oct 2004). U.S. Environmental Protection Agency, Washington, DC, EPA 600/P-99/002aF-bF, 2004Google Scholar
  56. Wang J, Martin ST (2007) Satellite characterization of urban aerosols: importance of including hygroscopicity and mixing state in the retrieval algorithms. J Geophys Res Atmos 112:1–18CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Earth and Atmospheric SciencesNational Institute of Technology RourkelaRourkelaIndia
  2. 2.State Pollution Control Board, Odisha, Regional Office, RourkelaRourkelaIndia
  3. 3.State Pollution Control Board, Odisha, Regional Office, AngulAngulIndia
  4. 4.State Pollution Control Board, Odisha, Regional Office, ParadeepParadeepIndia

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