Skip to main content

Advertisement

Log in

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

  • Original Article
  • Published:
Earth Systems and Environment Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • 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):544

    Article  Google Scholar 

  • 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 39:3719–3724

    Article  Google Scholar 

  • Briggs NL, Long CM (2016) Critical review of black carbon and elemental carbon source apportionment in Europe and the United States. Atmos Environ 144:409–427

    Article  Google Scholar 

  • Census Report (2011) The Registrar General & Census Commissioner, India (http://www.censusindia.gov.in/2011census/population_enumeration.html)

  • 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 13:6031–6048

    Google Scholar 

  • Cheng I, Zhang L, Xu X (2016) Impact of measurement uncertainties on receptor modeling of speciated atmospheric mercury. Sci Rep 6(20676):1–11

    Google Scholar 

  • CPCB Report (2009) National Ambient Air Quality Standards (NAAQS), Gazette Notifcation, New Delhi

  • CPCB Report (2015) National Air Quality Index. Series: CUPS/82/2014-15, pp 58

  • 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):603–617

    Article  Google Scholar 

  • Das S, Sarkar A, Das MK, Rahman MM, Islam MN (2015) Composite characteristics of Nor’westers based on observations and simulations. Atmos Res 158:158–178

    Article  Google Scholar 

  • 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 15:1979–1989

    Article  Google Scholar 

  • Dominici F, Greenstone M, Sunstein CR (2014) Particulate matter matters. Science 344:257–259

    Article  Google Scholar 

  • Draxler RR (1999) HYSPLIT4 user’s guide. NOAA Tech. Memo. ERL ARL-230, NOAA Air Resources Laboratory, Silver Spring, MD

  • 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 24

  • Draxler RR, Hess GD (1998) An overview of the HYSPLIT_4 modeling system for trajectories, dispersion, and deposition. Aust Meteor Mag 47:295–308

    Google Scholar 

  • 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 35:703–713

    Article  Google Scholar 

  • Gogikar P, Tyagi B (2016) Assessment of particulate matter variation during 2011–2015 over a tropical station Agra, India. Atmo Environ 147:11–21

    Article  Google Scholar 

  • 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):173–186

    Google Scholar 

  • Guttikunda SK, Gurjar BR (2012) Role of meteorology in seasonality of air pollution in megacity Delhi, India. Environ Monit Assess 184:3199–3211

    Article  Google Scholar 

  • Health Effects Institute (HEI) (2018) State of global air 2018. Special Report. Health Effects Institute, Boston

  • Hsu YK, Holsen TM, Hopke PK (2003) Comparison of hybrid receptor models to locate PCB sources in Chicago. Atmos Environ 37:545–562

    Article  Google Scholar 

  • 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. https://doi.org/10.1155/2013/264046

    Article  Google Scholar 

  • 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 120:475–483

    Article  Google Scholar 

  • 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 120:347–360

    Article  Google Scholar 

  • 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 285–289

  • 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 142:210–219

    Article  Google Scholar 

  • 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 131:135–148

    Article  Google Scholar 

  • 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):6521–6529

    Article  Google Scholar 

  • 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 196:16–25

    Article  Google Scholar 

  • 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 126:28–35

    Article  Google Scholar 

  • 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 148:215–229

    Article  Google Scholar 

  • MSME Report Balaghat (2012) Micro, Small and Medium enterprises development institute Udyog Vihar, Ministry of MSME, Government of India, p 12

  • MSME report Chandrapur (2012) Micro, Small and Medium enterprises development institute Nagpur, Ministry of MSME, Government of India, p 25

  • MSME Report Gondia (2012) Micro, Small and Medium enterprises development institute Nagpur, Ministry of MSME, Government of India, p 23

  • MSME Report Lohardaga (2012) Micro, Small and Medium enterprises development institute Ranchi, Ministry of MSME, Government of India, p 13

  • MSME Report Nagpur (2012) Micro, Small and Medium enterprises development institute Nagpur, Ministry of MSME, Government of India, p 18

  • MSME Report Sidhi (2012) Micro, Small and Medium enterprises development institute Udyog Vihar, Ministry of MSME, Government of India, p 16

  • MSME Report Singrauli (2016) Micro, Small and Medium enterprises development institute Indore, Ministry of MSME, Government of India, p 13

  • Nel A (2005) Air pollution-related illness: effects of particles. Science 308:804–806

    Article  Google Scholar 

  • 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 40:910–924

    Article  Google Scholar 

  • 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):6547–6555

    Article  Google Scholar 

  • 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):506–520

    Article  Google Scholar 

  • Rao MN, Rao HVN (1989) Air pollution indices: air pollution. Tata McGraw-Hill Publishing Ltd, New Delhi, pp 271–272

    Google Scholar 

  • 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 India

  • SAIL Annual Report (2016) Steel Authority of India Limited Annual report 2015-2016, p 164. https://www.sail.co.in/financial-list/103

  • 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):199–220

    Article  Google Scholar 

  • 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):1769–1778

    Article  Google Scholar 

  • 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, MA

    Chapter  Google Scholar 

  • 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 96:2059–2077

    Article  Google Scholar 

  • 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):537

    Google Scholar 

  • 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 28:114–120

    Article  Google Scholar 

  • 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 65:1745–1764

    Article  Google Scholar 

  • 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):13–28

    Article  Google Scholar 

  • Uria-Tellaetxe I, Carslaw DC (2014) Conditional bivariate probability function for source identification. Environ Model Soft 9:1–9

    Article  Google Scholar 

  • 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, 2004

  • 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 112:1–18

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bhishma Tyagi.

Ethics declarations

Conflict of interest

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gogikar, P., Tyagi, B., Padhan, R.R. et al. Particulate Matter Assessment Using In Situ Observations from 2009 to 2014 over an Industrial Region of Eastern India. Earth Syst Environ 2, 305–322 (2018). https://doi.org/10.1007/s41748-018-0072-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41748-018-0072-8

Keywords

Navigation