Abstract
PM2.5 (particulate matter size less than 2.5 µm, also called Respirable suspended particulate matter (RSPM)) is causing devastating effects on various living entities and is deleterious more than any other pollutants. As ambient air pollution is a scourge to India, in the present research work, PM2.5 is considered and the current study aims to estimate surface level PM2.5 concentrations using satellite-derived aerosol optical depth (AOD) along with meteorological data obtained from reanalysis and in-situ measurements over two different cities of India, namely: Agra, a non-industrial site for a study period of 2011–2015 and Rourkela, a highly industrialized location for 2009–2013, respectively. From the average daily variation of PM2.5, the pollution levels are critical and exceeding the threshold values defined by the pollution control board for most of the days at both the sites. Satellite-observed AOD values were also found to be very high over Agra (average AOD 0.76–0.8) and Rourkela (average AOD 0.4–0.46) during the study period. The annual exceedance factor (AEF) values over Agra and Rourkela were found to be always > 1.5 which indicates the above critical state of pollution. Traditional simple linear regression method (Model I), multiple linear regression (Model II (a–e)), log-linear regression (Model III) and conditional based MLR (Model IV and Model V) methods are applied to estimate the PM2.5 concentrations over Taj for Agra region for a study period of 2011–2015 and Sonaparbat for Rourkela region for a study period of 2009–2013. The models obtained over Taj and Sonaparbat are applied to Rambagh (2011–2015) and Rourkela (2009–2013) sites for validation. The coefficient of determination (R) between observed and estimated values are found to be statistically significant for model II (e) during training and validation at both the sites and model performance is adequate. The Model II (e) can thus be used as a unified explanatory model for the estimation of PM2.5 over these two monitoring stations.
Similar content being viewed by others
References
Agarwal A, Satsangi A, Lakhani A, Kumari KM (2020) Seasonal and spatial variability of secondary inorganic aerosols in PM2.5 at Agra: source apportionment through receptor models. Chemosphere 242:125–132
Agrawal M, Singh B, Rajput M, Marshall F, Bell JNB (2003) Effect of air pollution on peri-urban agriculture: a case study. Environ Pollut 126:323–329. https://doi.org/10.1016/S0269-7491(03)00245-8
Aw J, Kleeman MJ (2003) Evaluating the firstorder effect of intra-annual temperature variability on urban air pollution. J Geophy Res Atmospheres 108:(D12).
Badami MG (2005) Transport and urban air pollution in India. Environ Manage 36:195–204. https://doi.org/10.1007/s00267-004-0106-x
Census Report (2011) The registrar general and census commissioner. Government of India. https://www.censusindia.gov.in/2011census/population_enumeration.html. Accessed 15 Aug 2018
Chatterjee S, Hadi AS (2015) Regression analysis by example. Wiley, Hoboken
Chelani AB (2019) Estimating PM2.5 concentration from satellite derived aerosol optical depth and meteorological variables using a combination model. Atmos Pol Res 10(3):847–857
Chen BB, Sverdlik LG, Imashev SA, Solomon PA, Lantz J, Schauer JJ, Shafer MM, Artamonova MS, Carmichael G (2013) Empirical relationship between particulate matter and aerosol optical depth over Northern Tien-Shan, Central Asia. Air Qual Atmos Heal 6:385–396. https://doi.org/10.1007/s11869-012-0192-5
Chitranshi S, Sharma SP, Dey S (2015) Satellite-based estimates of outdoor particulate pollution (PM10) for Agra City in northern India. Air Qual Atmos Heal 8:55–65. https://doi.org/10.1007/s11869-014-0271-x
Chowdhury S, Dey S (2016) Cause-specific premature death from ambient PM2.5 exposure in India: estimate adjusted for baseline mortality. Environ Int 91:283–290. https://doi.org/10.1016/j.envint.2016.03.004
Chu DA, Kaufman YJ, Ichoku C, Remer LA, Tanré D, Holben B (2002) Validation of MODIS aerosol optical depth retrieval over land. Geophys Res Lett 29:4–7. https://doi.org/10.1029/2001GL013205
CPCB report (2009) National ambient air quality standards (NAAQS). Gazette Notifcation, New Delhi
CPCB Report (2013) Guidelines for the measurement of ambient air pollutants. http://mahenvis.nic.in/Pdf/Report/report_epm_NAAQMS%20.pdf. Accessed 17 Mar 2020
Dawson JP, Adams PJ, Pandis SN (2007) Sensitivity of PM2.5 to climate in the Eastern US: a modeling case study. Atmos Chem Phys 7:4295–4309. https://doi.org/10.5194/acp-7-4295-2007
Elangasinghe MA, Singhal N, Dirks KN, Salmond JA (2014) Development of an ANN-based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmos Pollut Res 5:696–708
Gogikar P, Tyagi B (2016) Assessment of particulate matter variation during 2011–2015 over a tropical station Agra, India. Atmos Environ 147:11–21. https://doi.org/10.1016/j.atmosenv.2016.09.063
Gogikar P, Tyagi B, Padhan RR, Mahaling M (2018a) Particulate matter assessment using in situ observations from 2009 to 2014 over an industrial region of Eastern India. Earth Syst Environ. https://doi.org/10.1007/s41748-018-0072-8
Gogikar P, Tyagi B, Gorai AK (2018b) Seasonal prediction of particulate matter over the steel city of India using neural network models. Model Earth Syst Environ 5(1):227–243. https://doi.org/10.1007/s40808-018-0530-1
Grgurić S, Križan J, Gašparac G, Antonić O, Špirić Z, Mamouri R, Christodoulou A, Nisantzi A, Agapiou A, Themistocleous K, Fedra K (2014) Relationship between MODIS based aerosol optical depth and PM10 over Croatia. Open Geosci 6(1):2–16
Gupta P, Christopher SA (2009) Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: multiple regression approach. J Geophys Res Atmos 114:1–13. https://doi.org/10.1029/2008JD011496
Gupta P, Christopher SA, Wang J, Gehrig R, Lee Y, Kumar N (2006) Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmos Environ 40:5880–5892. https://doi.org/10.1016/j.atmosenv.2006.03.016
https://www.sail.co.in/sites/default/files/investor/SAILAR201819.pdf. Accessed on 7 June 2020
Kavuri NC, Paul KK, Roy N (2013) Regression modeling of gaseous air pollutants and meteorological parameters in a steel city, Rourkela. ResJ Recent Sci 2:285–289
Kavuri NC, Paul KK, Roy N (2015) TSP aerosol source apportionment in the urban region of the Indian steel city, Rourkela. Particuology 20:124–133
Kim K, Lee KH, Kim JI, Noh Y, Shin DH, Shin SK, Lee D, Kim J, Kim YJ, Song CH (2016) Estimation of surface-level PM concentration from satellite observation taking into account the aerosol vertical profiles and hygroscopicity. Chemosphere 143:32–40. https://doi.org/10.1016/j.chemosphere.2015.09.040
Kisi O, Parmar KS, Soni K, Demir V (2017) Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Qual Atmos Health 10(7):873–883
Kleeman MJ (2007) A preliminary assessment of the sensitivity of air quality in California to global change. Clim Change. https://doi.org/10.1007/s10584-007-9351-3
Koelemeijer RBA, Homan CD, Matthijsen J (2006) Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmos Environ 40:5304–5315. https://doi.org/10.1016/j.atmosenv.2006.04.044
Kukkonen J, Pohjola M, Sokhi RS, Luhana L, Kitwiroon N, Fragkou L, Rantamäki M, Berge E, Odegaard V, Slordal LH, Denby B, Finardi S (2005) Analysis and evaluation of selected local-scale PM10 air pollution episodes in four European cities: Helsinki, London, Milan and Oslo. Atmos Environ 39:2759–2773. https://doi.org/10.1016/j.atmosenv.2004.09.090
Kulshrestha A, Satsangi PG, Masih J, Taneja A (2009) Metal concentration of PM2.5and PM10particles and seasonal variations in urban and rural environment of Agra. India Sci Total Environ 407:6196–6204. https://doi.org/10.1016/j.scitotenv.2009.08.050
Kumar N, Chu A, Foster A (2007) An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan. Atmos Environ 41:4492–4503
Kumar S, Srinivas N, Sunil KA (2014) Monitoring and assessment of air quality with reference to dust particles (PM10 and PM2.5) in urban environment. Int J Res Engi Techno 3:2321–7308
Lee HJ, Liu Y, Coull BA, Schwartz J, Koutrakis P (2011) A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmos Chem Phys 11:7991–8002. https://doi.org/10.5194/acp-11-7991-2011
Levy RC, Remer LA, Mattoo S, Vermote EF, Kaufman YJ (2007) Second-generation operational algorithm: retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. J Geophys Res Atmos 112:1–21. https://doi.org/10.1029/2006JD007811
Liu Y, Sarnat JA, Kilaru V, Jacob DJ, Koutrakis P (2005) Estimating ground level PM2.5 in the eastern United States using satellite remote sensing. Environ Sci Technol 39:3269–3278. https://doi.org/10.1021/es049352m
Liu Y, Franklin M, Kahn R, Koutrakis P (2006) Using aerosol optical thickness to predict ground level PM2.5 concentrations in the St. Louis area: a comparison between MISR and MODIS. Remote Sens Environ 107:33–44
Liu Y, Franklin M, Kahn R, Koutrakis P (2007) Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: a comparison between MISR and MODIS. Remote Sens Environ 107:33–44. https://doi.org/10.1016/j.rse.2006.05.022
Liu Y, Paciorek CJ, Koutrakis P (2009) Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environ Health Perspect 117:886–892. https://doi.org/10.1289/ehp.0800123
Liu C, Henderson BH, Wang D, Yang X, Peng ZR (2016) A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai. China Sci Tot Environ 565:607–615
Liu BC, Binaykia A, Chang PC, Tiwari MK, Tsao CC (2017) Urban air quality forecasting based on multi-dimensional collaborative support vector regression (svr): a case study of beijing-tianjin-shijiazhuang. PLoS ONE 12(7):e0179763
Luo J, Du P, Samat A, Xia J, Che M, Xue Z (2017) Spatiotemporal pattern of PM2.5 concentrations in Mainland China and analysis of its influencing factors using geographically weighted regression. Sci Rep 7:1–14. https://doi.org/10.1038/srep40607
Ma Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, Tong S, Bi J, Huang L, Liu Y (2016) Satellite-based spatiotemporal trends inPM2.5 concentrations: China, 2004–2013. Environ Health Perspect 124:184–192
Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, vol 821. Wiley, Hoboken
Niranjan K, Sreekanth V, Madhavan BL, Moorthy KK (2007) Aerosol physical properties and radiative forcing at the outflow region from the Indo-Gangetic plains during typical clear and hazy periods of wintertime. Geophys Res Lett 34:L19805. https://doi.org/10.1029/2007GL031224
Paciorek CJ, Liu Y (2009) Limitations of remotely sensed aerosol as a spatial proxy for fine particulate matter. Environ Heal Pers 117(6):904–909
Pant P, Lal RM, Guttikunda SK, Russell AG, Nagpure AS, Ramaswami A, Peltier RE (2019) Monitoring particulate matter in India: recent trends and future outlook. Air Qual Atmos Heal 12(1):45–58
Park ME, Song CH, Park RS, Lee J, Kim J, Lee S, Woo JH, Carmichael GR, Eck TF, Holben BN, Lee SS, Song CK, Hong Y (2014) New approach to monitor transboundary particulate pollution over Northeast Asia. Atmos Chem Phys 14:659–674. https://doi.org/10.5194/acp-14-659-2014
Phanikumar DV, Niranjan Kumar K, Shukla KK, Joshi H, Venkat Ratnam M, Naja M, Reddy K (2014) Signatures of Rossby wave modulations in aerosol optical depth over the central Himalayan region. Ann Geophys 32:175–180. https://doi.org/10.5194/angeo-32-175-2014
Pipal AS, Satsangi PG, Tiwari S, Taneja A (2014) Study of surface morphology, elemental composition and origin of atmospheric aerosols (PM2.5and PM10) over Agra. India Aerosol Air Qual Res 14:1685–1700. https://doi.org/10.4209/aaqr.2014.01.0017
Platnick S, Hubanks P, Meyer K, King MD (2015) MODIS atmosphere L3 monthly product (08_L3). NASA MODIS adaptive processing system, goddard space flight center https://dx.doi.org/10.5067/MODIS/MOD08_M3.006 (Terra) https://dx.doi.org/10.5067/MODIS/MYD08_M3.006 (Aqua)
Remer LA, Kaufman YJ, Tanré D, Mattoo S, Chu DA, Martins JV, Li RR, Ichoku C, Levy RC, Kleidman RG, Eck TF, Vermote E, Holben BN (2005) The MODIS aerosol algorithm, products, and validation. J Atmos Sci 62:947–973. https://doi.org/10.1175/JAS3385.1
Sah D, Verma PK, Kandikonda MK, Lakhani A (2019) Pollution characteristics, human health risk through multiple exposure pathways, and source apportionment of heavy metals in PM10 at Indo-Gangetic site. Urban Clim 27:149–162
Schaap M, Apituley A, Timmermans RMA, Koelemeijer RBA, De Leeuw G (2009) Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands. Atmos Chem Phys 9:909–925. https://doi.org/10.5194/acp-9-909-2009
Seinfeld JH, Pandis SN (2006) Atmospheric chemistry and physics. A Wiley-Inter Science Publication, Hoboken
Shaw N, Gorai AK (2018) Study of aerosol optical depth using satellite data (MODIS Aqua) over Indian Territory and its relation to particulate matter concentration. Environ Dev Sustain. https://doi.org/10.1007/s10668-018-0198-8
Song W, Jia H, Huang J, Zhang Y (2014) A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China. Remote Sens Environ 154:1–7. https://doi.org/10.1016/j.rse.2014.08.008
Soni M, Payra S, Verma S (2018) Particulate matter estimation over a semi arid region Jaipur, India using satellite AOD and meteorological parameters. Atmos Pollut Res 9:949–958. https://doi.org/10.1016/j.apr.2018.03.001
Sotoudeheian S, Arhami M (2014) Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran. J Environ Heal Sci Engi 12(1):1–13. https://doi.org/10.1186/s40201-014-0122-6
Sreekanth V, Mahesh B, Niranjan K (2017) Satellite remote sensing of fine particulate air pollutants over Indian mega cities. Adv Space Res 60(10):2268–2276
Tai APK, Mickley LJ, Jacob DJ (2010) Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: implications for the sensitivity of PM2.5 to climate change. Atmos Environ 44:3976–3984. https://doi.org/10.1016/j.atmosenv.2010.06.060
Tang CH, Coull BA, Schwartz J, Di Q, Koutrakis P (2017) Trends and spatial patterns of fine-resolution aerosol optical depth–derived PM2.5 emissions in the Northeast United States from 2002 to 2013. J Air Waste Manag Assoc 67:64–74. https://doi.org/10.1080/10962247.2016.1218393
Tian J, Chen D (2010) A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements. Remote Sens Environ 114:221–229. https://doi.org/10.1016/j.rse.2009.09.011
Trang NH, Tripathi NK (2014) Spatial correlation analysis between particulate matter 10 (PM10) hazard and respiratory diseases in chiang mai province, Thailand. Int Arch Photogramm Remote Sens Spat Inf Sci. https://doi.org/10.5194/isprsarchives-XL-8-185-2014
Van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R, Verduzco C, Villeneuve PJ (2010) Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ Health Perspect 118:847–855. https://doi.org/10.1289/ehp.0901623
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. https://doi.org/10.1029/2006JD008078
Wang J, Ogawa S (2015) Effects of meteorological conditions on PM2.5 concentrations in Nagasaki, Japan. Int J Environ Res Public Health 12:9089–9101. https://doi.org/10.3390/ijerph120809089
World Health Organization (2018) Air pollution and child health: prescribing clean air summary. WHO, Geneva, p 38
Xing YF, Xu YH, Shi MH, Lian YX (2016) The impact of PM2.5 on the human respiratory system. J Thoracic Dis 8(1):E69
Yap XQ, Hashim M (2013) A robust calibration approach for PM10 prediction from MODIS aerosol optical depth. Atmos Chem Phys 13:3517–3526. https://doi.org/10.5194/acp-13-3517-2013
Yeganeh B, Hewson MG, Clifford S, Knibbs LD, Morawska L (2017) A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques. Environ Model Softw 88:84–92. https://doi.org/10.1016/j.envsoft.2016.11.017
You W, Zang Z, Zhang L, Li Z, Chen D, Zhang G (2015) Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count. Remote Sens Environ 168:276–285
Zhang Q, Jiang X, Tong D, Davis SJ, Zhao H, Geng G, Feng T, Zheng B, Lu Z, Streets DG, Ni R, Brauer M, Van Donkelaar A, Martin RV, Huo H, Liu Z, Pan D, Kan H, Yan Y, Lin J, He K, Guan D (2017) Transboundary health impacts of transported global air pollution and international trade. Nature 543:705–709. https://doi.org/10.1038/nature21712
Acknowledgements
Ms Priyanjali Gogikar would like to acknowledge the National Institute of Technology Rourkela for providing fellowship for conducting research. Authors are thankful to Odisha State Pollution Control Board (OSPCB) for providing the datasets used in the present study. Authors also appreciate the constructive suggestions by anonymous reviewers and editor to improve the quality of manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gogikar, P., Tripathy, M.R., Rajagopal, M. et al. PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India. J Ambient Intell Human Comput 12, 2975–2991 (2021). https://doi.org/10.1007/s12652-020-02457-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02457-2