Abstract
PM10 (particular matter having size 10 µm or lesser) was sampled at three sites in Kozhikode district, India, and was found to vary between 20.83 and 83.8 µg m−3 at Site 1; 20.78 and 99.76 µg m−3 at Site 2; and 45.6 and 123.7 µg m−3 at Site 3. A dual-channel dust sampler was used for sampling. Average concentration (87.21 µg m−3) was found highest at Site 3. Among the species identified, Fe, Ca, SO42− and Na+ were the predominant ones in all the sites. A coupled receptor model used for source apportionment was initially tested using synthetic data and then used for experimental values. Sources predicted at Site 1 were paved road dust, marine aerosol, garden waste combustion, vehicular exhaust, and diesel generators, and their percentage contributions obtained were 56, 10.2, 27.2, 0.5, 6.1 from chemical mass balance and 50.8, 11.8, 22.2, 4.5, 10.7 from positive matrix factorization, respectively. In Site 2, fertilizer, vehicular exhaust, LPG combustion, marine aerosol and dust were the main sources with percentage contributions of 43.4, 9.6, 22.3, 0.5, 24.2 from chemical mass balance and 31.4, 24.7, 11.7, 2, 30.2 from positive matrix factorization, respectively. The model suggested four sources at Site 3: diesel generator, incineration, construction and vehicular exhaust with percentage contributions of 80, 0.5, 13.5, 6 from chemical mass balance and 74.8, 1.5, 9.4, 14.2 from positive matrix factorization, respectively. Stringent rules and regulations from the policymakers can curb source emissions to a great extent. Results from the present study can be used as a base to develop the same.
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References
Anu N, Rangabhashiyam S, Rahul A, Selvaraju N (2015) Evaluation of optimization methods for solving the receptor model for chemical mass balance. J Serbian Chem Soc 80:253–264. https://doi.org/10.2298/JSC131124052A
Bhuyan P, Deka P, Prakash A et al (2018) Chemical characterization and source apportionment of aerosol over Brahmaputra Valley. Environ Pollut 234:997–1010. https://doi.org/10.1016/j.envpol.2017.12.009
Bin Park M, Lee TJ, Lee ES, Kim DS (2019) Enhancing source identification of hourly PM2.5 data in Seoul based on a dataset segmentation scheme by positive matrix factorization (PMF). Atmos Pollut Res 10:1042–1059. https://doi.org/10.1016/j.apr.2019.01.013
Bove MC, Brotto P, Calzolai G et al (2016) PM10 source apportionment applying PMF and chemical tracer analysis to ship-borne measurements in the Western Mediterranean. Atmos Environ 125:140–151. https://doi.org/10.1016/j.atmosenv.2015.11.009
Bullock KR, Duvall RM, Norris GA et al (2008) Evaluation of the CMB and PMF models using organic molecular markers in fine particulate matter collected during the Pittsburgh Air Quality Study. Atmos Environ 42:6897–6904. https://doi.org/10.1016/j.atmosenv.2008.05.011
Cakmak S, Dales R, Marie L et al (2014) Metal composition of fine particulate air pollution and acute changes in cardiorespiratory physiology. Environ Pollut 189:208–214. https://doi.org/10.1016/j.envpol.2014.03.004
Chatterjee A, Sarkar C, Adak A et al (2013) Ambient air quality during diwali festival over Kolkata: a mega-city in India. Aerosol Air Qual Res 13:1133–1144. https://doi.org/10.4209/aaqr.2012.03.0062
Dai Q, Bi X, Huangfu Y et al (2019) A size-resolved chemical mass balance (SR-CMB) approach for source apportionment of ambient particulate matter by single element analysis. Atmos Environ 197:45–52. https://doi.org/10.1016/j.atmosenv.2018.10.026
Dvonch JT, Kannan S, Schulz AJ, Keeler GJ, Mentz G, House J, Benjamin A, Max P, Bard RL, Brook RD (2013) Acute effects of ambient particulate matter on blood pressure: differential effects across urban communities. Hypertension 53(5):853–859
Gargava P, Rajagopalan V (2016) Source apportionment studies in six Indian cities—drawing broad inferences for urban PM10 reductions. Air Qual Atmos Heal 9:471–481. https://doi.org/10.1007/s11869-015-0353-4
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 142:279–287. https://doi.org/10.1016/j.jhazmat.2006.08.013
Gupta I, Salunkhe A, Kumar R (2011) Source apportionment of PM10 by positive matrix factorization in Urban Area of Mumbai, India. Sci World J. https://doi.org/10.1100/2012/585791
Heo J, Hopke PK, Yi S (2009) Source apportionment of PM2.5 in Seoul, Korea. Atmos Chem Phys. https://doi.org/10.5194/acp-9-4957-2009
Herlekar M, Joseph AE, Kumar R, Gupta I (2012) Chemical speciation and source assignment of particulate (PM10) phase molecular markers in Mumbai. Aerosol Air Qual Res 12:1247–1260. https://doi.org/10.4209/aaqr.2011.07.0091
Jain S, Sharma SK, Mandal TK, Saxena M (2018) Source apportionment of PM10 in Delhi, India using PCA/APCS, UNMIX and PMF. Particuology 37:107–118. https://doi.org/10.1016/j.partic.2017.05.009
Kalaiarasan G, Balakrishnan RM, Sethunath NA, Manoharan S (2018) Source apportionment studies on particulate matter (PM10 and PM2.5) in ambient air of urban Mangalore, India. J Environ Manag 217:815–824. https://doi.org/10.1016/j.jenvman.2018.04.040
Keerthi R, Selvaraju N, Varghese LA et al (2018) Source apportionment studies for particulates (PM10) in Kozhikode, South Western India using a combined receptor model. Chem Ecol 34:1–21. https://doi.org/10.1080/02757540.2018.1508460
Khemani LT, Momin GA, Naik MS, Vijayakumar R, Ramana Murty BV (1982) Chemical composition and size distribution of atmospheric aerosols over the Deccan Plateau, India. Tellus 34:151–158. https://doi.org/10.1111/j.2153-3490.1982.tb01802.x
Kim E, Hopke PK, Edgerton ES et al (2003) Source identification of atlanta aerosol by positive matrix factorization source identification of atlanta aerosol by positive matrix factorization. J Air Waste Manag Assoc 53:731–739. https://doi.org/10.1080/10473289.2003.10466209
Kim KH, 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
Kong S, Han B, Bai Z et al (2010) Receptor modeling of PM2.5, PM10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China. Sci Total Environ 408:4681–4694. https://doi.org/10.1016/j.scitotenv.2010.06.005
Kothai P, Saradhi IV, Prathibha P et al (2008) Source apportionment of coarse and fine particulate matter at Navi Mumbai, India. Aerosol Air Qual Res 8:423–436. https://doi.org/10.2495/SDP-V9-N6-778-793
Lee S, Russell AG (2007) Estimating uncertainties and uncertainty contributors of CMB PM2. 5 source apportionment results. Atmos Environ 41:9616–9624. https://doi.org/10.1016/j.atmosenv.2007.08.022
Lee E, Chan CK, Paatero P (1999) Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong. Atmos Environ 33:3201–3212. https://doi.org/10.1016/S1352-2310(99)00113-2
Liu Y, Shao M, Lu S et al (2008) Source apportionment of ambient volatile organic compounds in the Pearl River Delta, China: Part II. Atmos Environ 42:6261–6274. https://doi.org/10.1016/j.atmosenv.2008.02.027
Lu Z, Liu Q, Xiong Y et al (2018) A hybrid source apportionment strategy using positive matrix factorization (PMF) and molecular marker chemical mass balance (MM-CMB) models. Environ Pollut 238:39–51. https://doi.org/10.1016/j.envpol.2018.02.091
Orogade SA, Owoade KO, Hopke PK et al (2016) Source apportionment of fine and coarse particulate matter in industrial areas of Kaduna Northern Nigeria. Aerosol Air Qual Res 16:1179–1190. https://doi.org/10.4209/aaqr.2015.11.0636
Paatero P, Tapper U (1994) Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5:111–126. https://doi.org/10.1002/env.3170050203
Paatero P, Eberly S, Brown SG, Norris GA (2014) Methods for estimating uncertainty in factor analytic solutions. Atmos Meas Tech 7:781–797. https://doi.org/10.5194/amt-7-781-2014
Pant P, Harrison RM (2012) Critical review of receptor modelling for particulate matter: a case study of India. Atmos Environ 49:1–12. https://doi.org/10.1016/j.atmosenv.2011.11.060
Parmar RS, Satsangi GS, Kumari M et al (2001) Study of size distribution of atmospheric aerosol at Agra. Atmos Environ 35:693–702. https://doi.org/10.1016/S1352-2310(00)00317-4
Pekney N, Davidson C, Robinson A et al (2006) Major source categories for PM2.5 in Pittsburgh using PMF and UNMIX. Aerosol Sci Technol 40:910–924. https://doi.org/10.1080/02786820500380271
Prakash J, Singhai A, Habib G et al (2017) Chemical characterization of PM1.0 aerosol in Delhi and source apportionment using positive matrix factorization. Environ Sci Pollut Res 24:445–462. https://doi.org/10.1007/s11356-016-7708-8
Reff A, Eberly SI, Bhave PV (2007) Receptor modeling of ambient particulate matter data using positive matrix factorization: review of existing methods. J Air Waste Manag Assoc 57:146–154. https://doi.org/10.1080/10473289.2007.10465319
Selvaraju N, Pushpavanam S (2010) Refining emission rate estimates using a coupled receptor-dispersion modeling approach. Atmos Environ 44:3935–3941. https://doi.org/10.1016/j.atmosenv.2010.07.011
Selvaraju N, Pushpavanam S, Anu N (2013) A holistic approach combining factor analysis, positive matrix factorization, and chemical mass balance applied to receptor modeling. Environ Monit Assess 185:10115–10129. https://doi.org/10.1007/s10661-013-3317-x
Sharma SK, Mandal TK, Jain S et al (2016) Source apportionment of PM2.5 in Delhi, India using PMF model. Bull Environ Contam Toxicol 97:286–293. https://doi.org/10.1007/s00128-016-1836-1
Song Y, Zhang Y, Xie S et al (2006) Source apportionment of PM2.5 in Beijing by positive matrix factorization. Atmos Environ 40:1526–1537. https://doi.org/10.1016/j.atmosenv.2005.10.039
Srimuruganandam B, Shiva Nagendra SM (2010) Analysis and interpretation of particulate matter—PM10, PM2.5 and PM1 emissions from the heterogeneous traffic near an urban roadway. Atmos Pollut Res 1:184–194. https://doi.org/10.5094/APR.2010.024
Srivastava D, Goel A, Agrawal M (2016) Particle bound metals at major intersections in an urban location and source identification through use of metal markers. Proc Natl Acad Sci India Sect A Phys Sci 86:209–220. https://doi.org/10.1007/s40010-016-0268-y
Taghvaee S, Sowlat MH, Mousavi A et al (2018) Source apportionment of ambient PM2.5 in two locations in central Tehran using the Positive Matrix Factorization (PMF) model. Sci Total Environ 628–629:672–686. https://doi.org/10.1016/j.scitotenv.2018.02.096
Watson JG, Chow JC, Lu Z et al (1994) Chemical mass balance source apportionment of PM10 during the Southern California Air Quality Study. Aerosol Sci Technol 21:1–36
Watson JG, Chow JC, Mathai CV (2012) Receptor models in air resources management: a summary of the APCA international specialty conference. J Air Pollut Control Assoc 39:419–426. https://doi.org/10.1080/08940630.1989.10466539
Wu C, Larson TV, Wu S et al (2007) Source apportionment of PM2.5 and selected hazardous air pollutants in Seattle. Sci Total Environ 386:42–52. https://doi.org/10.1016/j.scitotenv.2007.07.042
Xing YF, Xu YH, Shi MH, Lian YX (2016) The impact of PM2.5 on the human respiratory system. J Thorac Dis 8:E69–E74. https://doi.org/10.3978/j.issn.2072-1439.2016.01.19
Yang HH, Luo SW, Lee KT et al (2016) Fine particulate speciation profile and emission factor of municipal solid waste incinerator established by dilution sampling method. J Air Waste Manag Assoc 66:807–814. https://doi.org/10.1080/10962247.2016.1184195
Yu L, Wang G, Zhang R et al (2013) Characterization and source apportionment of PM2.5 in an urban environment in Beijing. Aerosol Air Qual Res 13:574–583. https://doi.org/10.4209/aaqr.2012.07.0192
Yubero E, Carratalá A, Crespo J et al (2011) PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain. Environ Sci Pollut Res 18:64–74. https://doi.org/10.1007/s11356-010-0352-9
Acknowledgement
This work was supported by the Kerala State Council for Science, Technology and Environment under the Environment and Ecology Project (Council Order No: 403/2015/KSCSTE). Swastik Laboratory, Ahmadabad, Sophisticated Analytical Instrument Facility, Mumbai, Sophisticated Instrumentation Center for Applied Research and Training, Ahmadabad, Sophisticated Analytical Instruments Facility, IIT Madras, have been acknowledged for their help in carrying out the analysis of filter papers. Mr. Nidheesh Kesav (business consultant at a leading technology firm) has been acknowledged for his guidance and assistance on using SPSS for statistical analysis and proofreading the paper, thereby improving the manuscript significantly.
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This work was supported by the Kerala State Council for Science, Technology and Environment under the Environment and Ecology Project (Council Order No: 403/2015/KSCSTE).
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Keerthi, K., Selvaraju, N. & Varghese, L.A. Use of combined receptor modeling technique for prediction of possible sources of particulate pollution in Kozhikode, India. Int. J. Environ. Sci. Technol. 17, 2623–2636 (2020). https://doi.org/10.1007/s13762-019-02553-7
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DOI: https://doi.org/10.1007/s13762-019-02553-7