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Atmospheric Particulate Matter Variations and Comparison of Two Forecasting Models for Two Indian Megacities

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Abstract

Ambient particulate matter levels influence air quality and are an environmental risk factor, especially in megacities. Meteorological factors modulate the ambient concentration of particulates in the atmosphere and hence, human exposure, visibility degradation, and other environmental effects. It is, therefore, crucial to forecast particulate matter levels. Various methods have been used to forecast atmospheric particulate matter level. The present study uses fine particulate matter (PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 micrometre) data from two megacities in South Asia to construct forecasting models—autoregressive (AR) and regression model and attempts to test the results. Delhi and Kolkata are the two cities for the study and data period is 2015–2017. Seasonal and diurnal trends are presented. Mean concentrations during the study period were highest in winter with daily concentrations exceeding the prescribed safe level by 3–7 times at both cities. Afternoon time was found to be the best time in a day with low particulate levels. Among meteorological parameters, temperature was found to have highest correlation (negative) with particulate matter at both the cities. Upon comparison with measured data for 15 days, it was found that AR model-estimated results do not exhibit the trend in the actual particulate levels, as is exhibited by the regression model estimations.

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References

  • Afzali A, Rashid M, Sabariah B, Ramli M (2014) PM10 pollution: its prediction and meteorological influence in Pasir Gudang, Johor. IOP Conf Ser: Earth Environ Sci 18:012100

    Article  Google Scholar 

  • Bhaduri S (2013) Vehicular growth and air quality at major traffic intersection points in Kolkata city: an efficient intervention strategies. SIJ Trans Adv Sp Res Earth Explor 1:19–25

    Google Scholar 

  • Bourdrel T, Bind MA, Béjot Y, Morel O, Argacha JF (2017) Cardiovascular effects of air pollution. Arch Cardiovasc Dis 110:634–642

    Article  Google Scholar 

  • CENR (2001) Air Quality Research Subcommittee of the Committee on Environment and Natural Resources (CENR). National Oceanic and Atmospheric Administration (NOAA). https://www.esrl.noaa.gov/csd/AQRS/reports/forecasting.pdf. Accessed 12 May 2017

  • Chattopadhyay G, Chattopadhyay S (2009) Autoregressive forecast of monthly total ozone concentration: a neurocomputing approach. Comput Geosci 35:1925–1932

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Cohen AJ, Brauer M, Burnett R et al (2017) Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet 389:1907–1918

    Article  Google Scholar 

  • Cukurluoglu S, Bacanli UG (2014) Time series analysis for the sulphur dioxide and particulate matter concentrations in the Aegean Region of Turkey. Int J Glob Warm 6:175–193

    Article  Google Scholar 

  • Das R, Khezri B, Srivastava B, Datta S, Sikdar PK, Webster RD, Wang X (2015) Trace element composition of PM2.5 and PM10 from Kolkata—a heavily polluted Indian metropolis. Atmos Pollut Res 6:742–750

    Article  Google Scholar 

  • Ghude SD, Chate DM, Jena C, Beig G, Kumar R, Barth MC, Pfister GG, Fadnavis S, Pithani P (2016) Premature mortality in India due to PM2.5 and ozone exposure. Geophys Res Lett 43:4650–4658

    Article  Google Scholar 

  • Gurjar BR, Ravindra K, Nagpure AS (2016) Air pollution trends over India megacities and their local-to-global implications. Atmos Environ 142:475–495

    Article  Google Scholar 

  • Guttikunda SK, Goel R, Pant P (2014) Nature of air pollution, emission sources, and management in the Indian cities. Atmos Environ 95:501–510

    Article  Google Scholar 

  • Gvozdić V, Brana J, Malatesti N, Puntarić D, Vidosavljević D, Roland D (2011) An analysis of the pollution problem in Slavonski Brod (eastern Croatia). Coll Antropol 35:1135–1141

    Google Scholar 

  • Hassanzadeh S, Hosseinibalam F, Alizadeh R (2009) Statistical models and time series forecasting of sulfur dioxide: a case study Tehran. Environ Monit Assess 155:149–155

    Article  Google Scholar 

  • Henriksson SV, Laaksonen A, Kerminen VM, Räisänen P, Järvinen H, Sundström AM, de Leeuw G (2011) Spatial distributions and seasonal cycles of aerosols in India and China seen in global climate-aerosol model. Atmos Chem Phys 11:7975–7990

    Article  Google Scholar 

  • Kovac-Andric E, Brana J, Gvozdic V (2009) Impact of meteorological factors on ozone concentrations modelled by time series analysis and multivariate statistical methods. Ecol Inform 4:117–122

    Article  Google Scholar 

  • Lengyel A, Héberger K, Paksy L, Bánhidi O, Rajkó R (2004) Prediction of ozone concentration in ambient air using multivariate methods. Chemosphere 57:889–896

    Article  Google Scholar 

  • Li J, Wang G, Wang X, Cao J, Sun T, Cheng C, Meng J, Hu T, Liu S (2013) Abundance, composition and source of atmospheric PM2.5 at a remote site in the Tibetan Plateau, China. Tellus B Chem Phys Meteorol 65:20281

    Article  Google Scholar 

  • Liu Z, Hu B, Wang L, Wu F, Gao W, Wang Y (2015) Seasonal and diurnal variation in particulate matter (PM10 and PM2.5) at an urban site of Beijing: analyses from a 9-year study. Environ Sci Pollut Res 22:627–642

    Article  Google Scholar 

  • Lou C, Liu H, Li Y, Peng Y, Wang J, Dai L (2017) Relationships of relative humidity with PM2.5 and PM10 in the Yangtze River Delta, China. Environ Monit Assess 189:582

    Article  Google Scholar 

  • Mishra D, Goyal P (2015) Estimation of vehicular emissions using dynamic emission factors: a case study of Delhi, India. Atmos Environ 98:1–7

    Article  Google Scholar 

  • Ocak S, Turalioglu FS (2008) Effect of meteorology on the atmospheric concentrations of traffic-related pollutants in Erzurum, Turkey. J Int Environ Appl Sci 3:325–335

    Google Scholar 

  • Pant P, Shukla A, Kohl SD, Chow JC, Watson JG, Harrison RM (2015) Characterization of ambient PM2.5 at a pollution hotspot in New Delhi, India and inference of sources. Atmos Environ 109:178–189

    Article  Google Scholar 

  • Pope CA III, Dockery DW (2006) Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag Assoc 56:709–742

    Article  Google Scholar 

  • Pope CA III, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K et al (2002) Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J Am Med Assoc 287:1132–1141

    Article  Google Scholar 

  • Ramachandran S (2007) Aerosol optical depth and fine mode fraction variations deduced from Moderate Resolution Imaging Spectroradiometer (MODIS) over four urban areas in India. J Geophys Res 112:D16207

    Article  Google Scholar 

  • Sharma SK, Mandal TK, Srivastava M, Chatterjee KA, Jain S, Saxena M, Singh BP, Saraswati Sharma A, Adak A, Ghosh SK (2016) Spatio-temporal variation in chemical characteristics of PM10 over Indo Gangetic Plain of India. Environ Sci Pollut Res 23:18809–18822

    Article  Google Scholar 

  • Sousa S IV, Martins FG, Pereira MC, Alvim-Ferraz MCM (2006) Prediction of ozone concentrations in Oporto city with statistical approaches. Chemosphere 64:1141–1149

    Article  Google Scholar 

  • Tiwari S, Srivastava AK, Bishta DS, Parmita P, Srivastava MK, Attri SD (2013) Diurnal and seasonal variations of black carbon and PM2.5 over New Delhi, India: influence of meteorology. Atmos Res 125–126:50–62

    Article  Google Scholar 

  • UN (2016) The World’s Cities in 2016. United Nations. http://www.un.org/en/development/desa/population/publications/pdf/urbanization/the_worlds_cities_in_2016_data_booklet.pdf. Accessed 1 Aug 2017

  • 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

    Article  Google Scholar 

  • World Health Organization (WHO) (2014) Non communicable Diseases (NCD) Country Profiles. http://www.who.int/nmh/countries/npl_en.pdf. Accessed 17 Jan 2017

  • Yang Q, Yuan Q, Li T, Shen H, Zhang L (2017) The relationships between PM2.5 and meteorological factors in China: seasonal and regional variations. Int J Environ Res Public Health 14:1510

    Article  Google Scholar 

Download references

Acknowledgements

The United States Embassy and Consulates in India is duly acknowledged for providing PM2.5 concentration data. RB acknowledges the stipend by Indian Statistical Institute during the course period. The authors are thankful to the two anonymous reviewers for their constructive suggestions.

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Correspondence to D. S. Jyethi.

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Bhakta, R., Khillare, P.S. & Jyethi, D.S. Atmospheric Particulate Matter Variations and Comparison of Two Forecasting Models for Two Indian Megacities. Aerosol Sci Eng 3, 54–62 (2019). https://doi.org/10.1007/s41810-019-00041-6

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