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Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models

  • Srishti Jain
  • Sudhir Kumar Sharma
  • Manoj Kumar Srivastava
  • Abhijit Chaterjee
  • Rajeev Kumar Singh
  • Mohit Saxena
  • Tuhin Kumar Mandal
Article

Abstract

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM10 sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM10. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM10 concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 μg m−3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM10 mass concentration over the IGP region of India.

Notes

Acknowledgments

The authors are thankful to the Director, CSIR-NPL, New Delhi, and Head, Environmental Sciences and Biomedical Metrology Division, CSIR-NPL, New Delhi, for their encouragement and support for this study. The authors also acknowledge Council of Scientific and Industrial Research (CSIR), New Delhi, for providing financial support for this study (under CSIR-EMPOWER Project: OLP-102132). One of the authors, Srishti Jain, thankfully acknowledge the Department of Science and Technology (DST), New Delhi, for awarding the INSPIRE Fellowship.

Supplementary material

244_2018_572_MOESM1_ESM.docx (104 kb)
Supplementary material 1 (DOCX 103 kb)

References

  1. Amato F, Pandolfi M, Escrig A, Querol X, Alastuey A, Pey J, Pérez N, Hopke PK (2009) Quantifying road dust re-suspension in urban environment by multi linear engine: a comparison with PMF2.5. Atmos Environ 43:2770–2780CrossRefGoogle Scholar
  2. Andreae MO, Merlet P (2001) Emission of trace gases and aerosols from biomass burning. Glob Biogeochem Cycles 15:955–966CrossRefGoogle Scholar
  3. Balachandran S, Meena BR, Khillare PS (2000) Particle size distribution and its elemental composition in the ambient air of Delhi. Environ Int 26(1):49–54CrossRefGoogle Scholar
  4. Banerjee T, Murari V, Kumar M, Raju MP (2015) Source apportionment of airborne particulates through receptor modeling: Indian scenario. Atmos Res 164:167–187CrossRefGoogle Scholar
  5. Begum BA, Biswas SK, Markwitz A, Hopke PK (2010) Identification of sources of fine and coarse particulate matter in Dhaka, Bangladesh. Aerosol Air Qual Res 10(4):345–353CrossRefGoogle Scholar
  6. Begum BA, Biswas SK, Hopke PK (2011) Key issues in controlling air pollutants in Dhaka, Bangladesh. Atmos Environ 45(40):7705–7713CrossRefGoogle Scholar
  7. Belis CA, Karagulian F, Larsen BR, Hopke PK (2013) Critical review and meta-analysis of ambient particulate matter source apportionment using receptor models in Europe. Atmos Environ 69:94–108CrossRefGoogle Scholar
  8. Beuck H, Quass U, Klemm O, Kuhlbusch TAJ (2011) Assessment of sea salt and mineral dust contributions to PM10 in NW Germany using tracer models and positive matrix factorization. Atmos Environ 45(32):5813–5821CrossRefGoogle Scholar
  9. Brauer M, Freedman G, Frostad J, Van Donkelaar A, Martin RV, Dentener F, Balakrishnan K (2015) Ambient air pollution exposure estimation for the global burden of disease 2013. Environ Sci Technol 50(1):79–88CrossRefGoogle Scholar
  10. Cesari D, Amato F, Pandolfi M, Alastuey A, Querol X, Contini D (2016) An inter-comparison of PM10 source apportionment using PCA and PMF receptor models in three European sites. Environ Sci Pollut Res 23(15):15133–15148CrossRefGoogle Scholar
  11. Cesari D, De Benedetto GE, Bonasoni P, Busetto M, Dinoi A, Merico E, Marinoni A (2018) Seasonal variability of PM2.5 and PM10 composition and sources in an urban background site in Southern Italy. Sci Total Environ 612:202–213CrossRefGoogle Scholar
  12. Chakraborty A, Gupta T (2010) Chemical characterization and source apportionment of submicron (PM1) aerosol in Kanpur region, India. Aerosol Air Qual Res 10(5):433–445CrossRefGoogle Scholar
  13. Chan TW, Mozurkewich M (2007) Simplified representation of atmospheric aerosol size distributions using absolute principal component analysis. Atmos Chem Phys 7(3):875–886CrossRefGoogle Scholar
  14. Chen LWA, Watson JG, Chow JC, Magliano KL (2007) Quantifying PM2.5 source contributions for the San Joaquin Valley with multivariate receptor models. Environ Sci Technol 41(8):2818–2826CrossRefGoogle Scholar
  15. Chow JC, Watson JG, Chen LWA, Arnott WP, Moosmuller H (2004) Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environ Sci Techno 38:4414–4422CrossRefGoogle Scholar
  16. Contini D, Belosi F, Gambaro A, Cesari D, Stortini AM, Bove MC (2012) Comparison of PM10 concentrations and metal content in three different sites of the Venice Lagoon: an analysis of possible aerosol sources. J Environ Sci 24(11):1954–1965CrossRefGoogle Scholar
  17. Cusack M, Pérez N, Pey J, Alastuey A, Querol X (2013) Source apportionment of fine PM and sub-micron particle number concentrations at a regional background site in the western Mediterranean: a 2.5 year study. Atmos Chem Phys 13(10):5173–5187CrossRefGoogle Scholar
  18. Dachs J, Eisenreich SJ (2000) Adsorption onto aerosol soot carbon dominates gas-particle partitioning of polycyclic aromatic hydrocarbons. Environ Sci Tech 34(17):3690–3697CrossRefGoogle Scholar
  19. Das M, Maiti SK, Mukhopadhyay U (2006) Distribution of PM2.5 and PM10–2.5 in PM10 fraction in ambient air due to vehicular pollution in Kolkata megacity. Environ Monit Assess 122(1–3):111–123CrossRefGoogle Scholar
  20. EPA PMF User Guide (2008) EPA Positive matrix Factorization (PMF) 3.0 Fundamentals and User Guide. US-EP Office of Research and DevelopmentGoogle Scholar
  21. Favez O, Haddad IE, Piot C, Boréave A, Abidi E, Marchand N, Wortham H (2010) Inter-comparison of source apportionment models for the estimation of wood burning aerosols during wintertime in an Alpine city (Grenoble, France). Atmos Chem Phys 10(12):5295–5314CrossRefGoogle Scholar
  22. García JH, Li WW, Cárdenas N, Arimoto R, Walton J, Trujillo D (2006) Determination of PM2.5 sources using time-resolved integrated source and receptor models. Chemosphere 65(11):2018–2027CrossRefGoogle Scholar
  23. Goyal P, Sidhartha (2002) Effect of winds on SO2 and SPM concentration in Delhi. Atmos Environ 36:2925–2930CrossRefGoogle Scholar
  24. Gugamsetty B, Wei H, Liu CN, Awasthi A, Hsu SC, Tsai CJ et al (2012) Source characterization and apportionment of PM10, PM2.5 and PM0.1 by using positive matrix factorization. Aerosol Air Qual Res 12:476–491CrossRefGoogle Scholar
  25. Gupta AK, Karar K, Srivastava A (2007) Chemical mass balance source apportionment of PM10 and TSP in residential and industrial sites of an urban region of Kolkata, India. J Hazard Mater 142(1):279–287CrossRefGoogle Scholar
  26. Guttikunda SK, Lodoysamba S, Bulgansaikhan B, Dashdondog B (2013) Particulate pollution in Ulaanbaatar, Mongolia. Air Qual Atmos Heal 6:589–601.  https://doi.org/10.1007/s11869-013-0198-7 CrossRefGoogle Scholar
  27. Harrison RM, Beddows DC, Dall’Osto M (2011) PMF analysis of wide range particle size spectra collected on a major highway. Environ Sci Technol 45(13):5522–5528CrossRefGoogle Scholar
  28. Henry RC (1997) History and fundamentals of multivariate air quality receptor models. Chemom Intell Lab Syst 37(1):37–42CrossRefGoogle Scholar
  29. Henry RC (2003) Multivariate receptor modeling by N-dimensional edge detection. Chemom Intell Lab Syst 65(2):179–189CrossRefGoogle Scholar
  30. Ho KF, Lee SC, Chow JC, Watson JG (2003) Characterization of PM10 and PM2.5 source profiles for fugitive dust in Hong Kong. Atmos Environ 37(8):1023–1032CrossRefGoogle Scholar
  31. Hopke PK (2016) Review of receptor modeling methods for source apportionment. J Air Waste Manag Assoc 66(3):237–259CrossRefGoogle Scholar
  32. Hopke PK, Ito K, Mar T, Christensen WF, Eatough DJ, Henry RC et al (2006) PM source apportionment and health effects: 1. Intercomparison of source apportionment results. J Exp Sci Environ Epidemiol 16(3):275–286CrossRefGoogle Scholar
  33. IPCC (2013) Intergovernmental panel on climate change; technical summary. Climate change 2013, the physical science basis. Contribution of working group I to the fifth assessment report, pp 33–115.  https://doi.org/10.1017/cbo9781107415324.005
  34. Jaeckels JM, Bae MS, Schauer JJ (2007) Positive matrix factorization (PMF) analysis of molecular marker measurements to quantify the sources of organic aerosols. Environ Sci Techno 41(16):5763–5769CrossRefGoogle Scholar
  35. Jain S, Sharma SK, Choudhary N, Masiwal R, Saxena M, Sharma A et al (2017a) Chemical characteristics and source apportionment of PM2.5 using PCA/APCS UNMIX and PMF at an urban site of Delhi, India. Environ Sci Pollut Res 24(17):14637–14656CrossRefGoogle Scholar
  36. Jain S, Sharma SK, Mandal TK, Saxena M (2017b) Source apportionment of PM10 in Delhi, India using PCA/APCS, UNMIX and PMF. Particuology.  https://doi.org/10.1016/j.partic.2017.05.009 CrossRefGoogle Scholar
  37. Jaiprakash Singhai A, Habib G, Raman RS, Gupta T (2017) Chemical characterization of PM1.0 aerosol in Delhi and source apportionment using positive matrix factorization. Environ Sci Pollut Res 24(1):445–462CrossRefGoogle Scholar
  38. Jeong JH, Shon ZH, Kang M, Song SK, Kim YK, Park J, Kim H (2017) Comparison of source apportionment of PM2.5 using receptor models in the main hub port city of East Asia: Busan. Atmos Environ 148:115–127CrossRefGoogle Scholar
  39. Karagulian F, Belis CA, Dora CFC, Prüss-Ustün AM, Bonjour S, Adair-Rohani H, Amann M (2015) Contributions to our cities air pollution: a global analysis of field studies for health impact consideration. Atmos Environ 120:475–483CrossRefGoogle Scholar
  40. Karar K, Gupta AK (2007) Source apportionment of PM10 at residential and industrial sites of an urban region of Kolkata. India. Atmos Res 84(1):30–41CrossRefGoogle Scholar
  41. Khare P, Baruah BP (2010) Elemental characterization and source identification of PM2.5 using multivariate analysis at the suburban site of north-east India. Atmos Res 98(1):148–162CrossRefGoogle Scholar
  42. Khillare PS, Balachandran S, Meena BR (2004) Spatial and temporal variation of heavy metals in atmospheric aerosol of Delhi. Environ Monit Asses 90(1–3):1–21CrossRefGoogle Scholar
  43. Kong S, Ding X, Bai Z, Han B, Chen L, Shi J, Li Z (2010) A seasonal study of polycyclic aromatic hydrocarbons in PM2.5 and PM2.5–10 in five typical cities of Liaoning Province, China. J Hazard Mater 183(1):70–80CrossRefGoogle Scholar
  44. Kothai P, Saradhi IV, Prathibha P, Hopke PK, Pandit GG, Puranik VD (2008) Source apportionment of coarse and fine particulate matter at Navi Mumbai, India. Aerosol Air Qual Res 8(4):423–436CrossRefGoogle Scholar
  45. Kulshrestha A, Satsangi PG, Masih J, Taneja A (2009) Metal concentration of PM2.5and PM10 particles and seasonal variations in urban and rural environment of Agra, India. Sci Total Environ 407(24):6196–6204CrossRefGoogle Scholar
  46. Kumar AV, Patil RS, Nambi KSV (2001) Source apportionment of suspended particulate matter at two traffic junctions in Mumbai, India. Atmos Environ 35(25):4245–4251CrossRefGoogle Scholar
  47. Lee JH, Yoshida Y, Turpin BJ, Hopke PK, Poirot RL, Lioy PJ, Oxley JC (2002) Identification of sources contributing to mid-Atlantic regional aerosol. J Air Waste Manag Assoc 52(10):1186–1205CrossRefGoogle Scholar
  48. 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
  49. Mandal P, Saud T, Sarkar R, Mandal A, Sharma SK, Mandal TK et al (2014) High seasonal variation of atmospheric C and particle concentrations in Delhi, India. Environ Chem Lett 12(1):225–230CrossRefGoogle Scholar
  50. Manousakas M, Papaefthymiou H, Diapouli E, Migliori A, Karydas AG, Bogdanovic Radovic I, Eleftheriadis K (2017) Assessment of PM2.5 sources and their corresponding level of uncertainty in a coastal urban area using EPA PMF 5.0 enhanced diagnostics. Sci Total Environ 574:155–164CrossRefGoogle Scholar
  51. Murari V, Kumar M, Barman SC, Banerjee T (2015) Temporal variability of MODIS aerosol optical depth and chemical characterization of airborne particulates in Varanasi, India. Environ Sci Pollut Res 22(2):1329–1343CrossRefGoogle Scholar
  52. Ogundele LT, Owoade OK, Olise FS, Hopke PK (2016) Source identification and apportionment of PM2.5 and PM2.5-10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models. Environ Monit Assess 188(10):574CrossRefGoogle Scholar
  53. Paatero P (1999) The multilinear engine—a table-driven, least squares program for solving multilinear problems, including the n-way parallel factor analysis model. J Comput Gr Stat 8(4):854–888Google Scholar
  54. Paatero P, Tapper U (1994) Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2):111–126CrossRefGoogle Scholar
  55. Pachauri T, Satsangi A, Singla V, Lakhani A, Kumari KM (2013) Characteristics and sources of carbonaceous aerosols in PM2.5 during wintertime in Agra, India. Aerosol Air Qual Res 13(3): 977–991CrossRefGoogle Scholar
  56. Pandey P, Patel DK, Khan AH, Barman SC, Murthy RC, Kisku GC (2013) Temporal distribution of fine particulates (PM2.5, PM10), potentially toxic metals, PAHs and metal-bound carcinogenic risk in the population of Lucknow City, India. J Environ Sci Health A 48(7):730–745CrossRefGoogle Scholar
  57. Pant P, Harrison RM (2012) Critical review of receptor modeling for particulate matter: a case study of India. Atmos Environ 49:1–12CrossRefGoogle Scholar
  58. Perrino C, Tiwari S, Catrambone M, Dalla Torre S, Rantica E, Canepari S (2011) Chemical characterization of atmospheric PM in Delhi, India, during different periods of the year including Diwali festival. Atmos Pollut Res 2(4):418–427CrossRefGoogle Scholar
  59. Pope CA III, Ezzati M, Dockery DW (2009) Fine-particulate air pollution and life expectancy in the United States. N Engl J Med 360(4):376–386CrossRefGoogle Scholar
  60. Ram K, Sarin MM (2010) Spatio-temporal variability in atmospheric abundances of EC, OC and WSOC over Northern India. J Aerosol Sci 41(1):88–98CrossRefGoogle Scholar
  61. Raman RS, Ramachandran S (2010) Annual and seasonal variability of ambient aerosols over an urban region in western India. Atmos Environ 44(9):1200–1208CrossRefGoogle Scholar
  62. Ramgolam K, Favez O, Cachier H, Gaudichet A, Marano F (2009) Size-partitioning of an urban aerosol to identify particle determinants involved in the pro inflammatory response induced in airway epithelial cells. Part Fibre Toxicol 6:1–12CrossRefGoogle Scholar
  63. Robinson AL, Subramanian R, Donahue NM, Bernardo-Bricker A, Rogge WF (2006) Source apportionment of molecular markers and organic aerosol. 2. Biomass smoke. Environ Sci Technol 40:7811–7819CrossRefGoogle Scholar
  64. Saraswati Sharma SK, Mandal TK (2017) Five-year measurements of ambient ammonia and its relationships with other trace gases at an urban site of Delhi, India. Meteorol Atmos Phys 130(2):241–257CrossRefGoogle Scholar
  65. Seinfeld JH, Pandis SN (2016) Atmospheric chemistry and physics: from air pollution to climate change. Wiley, New YorkGoogle Scholar
  66. Sen A, Ahammed YN, Banerjee T, Chatterjee A, Choudhuri AK, Das T, Mandal TK (2016) Spatial variability in ambient atmospheric fine and coarse mode aerosols over Indo-Gangetic plains, India and adjoining oceans during the onset of summer monsoons. Atmos Pollut Res 7(3):521–532CrossRefGoogle Scholar
  67. Sharma DN, Sawant AA, Uma R, Cocker DR (2003) Preliminary chemical characterization of particle-phase organic compounds in New Delhi, India. Atmos Environ 37(30):4317–4323CrossRefGoogle Scholar
  68. Sharma SK, Mandal TK, Saxena M, Sharma A, Datta A, Saud T (2014a) Variation of OC, EC, WSIC and trace metals of PM10 in Delhi, India. J Atmos Solar Terrestrial Phys 113:10–22CrossRefGoogle Scholar
  69. Sharma SK, Mandal TK, Saxena M, Sharma A, Gautam R (2014b) Source apportionment of PM10 by using positive matrix factorizationat an urban site of Delhi, India. Urban Climate 10:656–670CrossRefGoogle Scholar
  70. Sharma SK, Sharma A, Saxena M, Choudhary N, Masiwal R, Mandal TK et al (2015) Chemical characterization and source apportionment of aerosol at an urban area of central Delhi, India. Atmos Pollut Res 7:110–121CrossRefGoogle Scholar
  71. Sharma SK, Mandal TK, Jain S, Saraswati, Sharma A, Saxena M (2016a) Source apportionment of PM2.5 in Delhi, India Using PMF Model. Bull Environ Contam Toxicol 97(2):286–293CrossRefGoogle Scholar
  72. Sharma SK, Mandal TK, Srivastava MK, Chatterjee A, Jain S, Saxena M et al (2016b) Spatio-temporal variation in chemical characteristics of PM10 over Indo Gangetic Plain of India. Environ Sci Pollut Res 23(18):18809–18822CrossRefGoogle Scholar
  73. Sharma SK, Agarwal P, Mandal TK, Karapurkar SG, Shenoy DM, Peshin SK et al (2017) Study on ambient air quality of megacity Delhi, India during odd-even strategy. MAPAN 32(2):155–165CrossRefGoogle Scholar
  74. Sharma SK, Mandal TK, Sharma A, Jain S, Saraswati (2018a) Carbonaceous species of PM2.5 in megacity Delhi, India during 2012–2016. Bull Environ Contamin Toxicol 100:695–701CrossRefGoogle Scholar
  75. Sharma SK, Mandal TK, Sharma A, Saraswati, Jain S (2018b) Seasonal and annual trends of carbonaceous species in PM10 over a megacity Delhi, India during 2010–2017. J Atmos Chem.  https://doi.org/10.1007/s10874-018-9379-y CrossRefGoogle Scholar
  76. Shi GL, Liu GR, Peng X, Wang YN, Tian YZ, Wang W, Feng YC (2014) A comparison of multiple combined models for source apportionment, including the PCA/MLR-CMB, UNMIX-CMB and PMFCMB models. Aerosol Air Qual Res 14(7):2040–2050CrossRefGoogle Scholar
  77. Shridhar V, Khillare PS, Agarwal T, Ray S (2010) Metallic species in ambient particulate matter at rural and urban location of Delhi. J Hazard Mater 175(1):600–607CrossRefGoogle Scholar
  78. Song Y, Xie S, Zhang Y, Zeng L, Salmon LG, Zheng M (2006) Source apportionment of PM2.5 in Beijing using principal component analysis/absolute principal component scores and UNMIX. Sci Total Environ 372(1):278–286CrossRefGoogle Scholar
  79. Srimuruganandam B, Nagendra SS (2012) Source characterization of PM10 and PM2.5 mass using a chemical mass balance model at urban roadside. Sci Total Environ 433:8–19CrossRefGoogle Scholar
  80. Thurston GD, Spengler JD (1985) A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston. Atmos Environ 19(1):9–25CrossRefGoogle Scholar
  81. Tiwari S, Chate DM, Srivastaua AK, Bisht DS, Padmanabhamurty B (2012) Assessments of PM1, PM2.5 and PM10 concentrations in Delhi at different mean cycles. Geofizika 29(2):125–141Google Scholar
  82. Tiwari S, Pervez S, Cinzia P, Bisht DS, Kumar A, Chate DM (2013) Chemical characterization of atmospheric particulate matter in Delhi, India, Part II: Source apportionment studies using PMF 3.0. Sustainable Environ Res 23(5):295–306Google Scholar
  83. Viana M, Kuhlbusch TAJ, Querol X, Alastuey A, Harrison RM, Hopke PK et al (2008) Source apportionment of particulate matter in Europe: a review of methods and results. J Aerosol Sci 39(10):827–849CrossRefGoogle Scholar
  84. WHO (2014) Ambient (outdoor) air pollution in cities database 2014.World Health Organization http://www.who.int/phe/healthtopics/outdoorair/databases/AAP_database_results_2014.pdf. Accessed 11 Nov 2017
  85. Yin J, Harrison RM, Chen Q, Rutter A, Schauer JJ (2010) Source apportionment of fine particles at urban background and rural sites in theUK atmosphere. Atmos Environ 44(6):841–851CrossRefGoogle Scholar
  86. Zhang R, Jing J, Tao J, Hsu SC, Wang G, Cao J et al (2013) Chemical characterization and source apportionment of PM2.5in Beijing: seasonal perspective. Atmos Chem Phys 13(14):7053–7074Google Scholar
  87. Zheng M, Cass GR, Ke L, Wang F, Schauer JJ, Edgerton ES, Russell AG (2007) Source apportionment of daily fine particulate matter at Jefferson Street, Atlanta, GA, during summer and winter. J Air Waste Manag Assoc 57(2):228–242CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Srishti Jain
    • 1
    • 2
  • Sudhir Kumar Sharma
    • 1
    • 2
  • Manoj Kumar Srivastava
    • 3
  • Abhijit Chaterjee
    • 4
  • Rajeev Kumar Singh
    • 3
  • Mohit Saxena
    • 1
  • Tuhin Kumar Mandal
    • 1
    • 2
  1. 1.Environmental Sciences and Biomedical Metrology DivisionCSIR-National Physical LaboratoryNew DelhiIndia
  2. 2.Academy of Scientific and Innovative Research (AcSIR), CSIR-National Physical Laboratory CampusNew DelhiIndia
  3. 3.Department of GeophysicsBanaras Hindu University (BHU)VaranasiIndia
  4. 4.Environmental Sciences SectionBose InstituteKolkataIndia

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