Document Type: ORIGINAL RESEARCH PAPER

Authors

1 Department of Mechanical Engineering, Indian Institute of Technology, Varanasi, India

2 Department of Mechanical Engineering, Brahmdevdata Mane Institute of Technology, Solapur, India

Abstract

The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter particles as PM2.5 and PM10 are the pollutants on which the study focuses. Mann-Kendall and Sen’s slope estimator tests are used on summer (February-May), monsoon (June-September) and winter (October-January) seasonal data from year 2013 to 2016 and trend results and power of the slopes are estimated.  For predictive analysis, different autoregressive integrated moving average models are compared with goodness of fit statistics, and the observed results stated autoregressive integrated moving average (1,1,1) as the best-suited for forecast modeling of different pollutants in Varanasi. Autoregressive integrated moving average model (1,1,1) is also used on the annual concentration levels to predict forthcoming year's annual pollutants value. Study reveals that PM 10 shows a rising trend with predicted approximate annual concentration of 273 µg/m3 and PM2.5, carbon monoxide, nitrogen dioxide and  sulphur dioxide show a reducing trend with approximate annual concentration of 139 µg/m3, 1.37 mg/ m3, 38 µg/m3 and 17 µg/m3, respectively, by the year 2030. The study predicted carbon monoxide, nitrogen dioxide andsulphur dioxide concentrations are lower and PM10 and PM2.5 concentrations are much higher to the standard permissible limits in future years also, and specific measures are required to control emissions of these pollutants in Varanasi.

Graphical Abstract

Highlights

  • Increasing trend in concentration of particulate matter particle of size up to 10 µm (PM 10) and decreasing trend for PM 2.5, CO, NO2 and SO2 in Varanasi
  • ARIMA (1,1,1) model is best suited for predicting the concentration of air pollutants for upcoming years in Varanasi
  • High concentration levels of PM 10 and PM 2.5 in future years suggest requirement of strict measures to control the emissions of these pollutants.

Keywords

Main Subjects

Aleksandropoulou, V.; Lazaridis, M., (2004). Spatial distribution of gaseous and particulate matter emissions in Greece. Water Air Soil Pollut., 153(1-4): 15-34 (20 pages).

Azmi, S.Z.; Latif, M.T.; Ismail, A.S.; Juneng, L.; Jemain, A.A., (2010). Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia. Air Qual. Atmos. Health., 3(1): 53-64 (12 pages).

Bansal, G.; Bandivadekar, A., (2013). Overview of India’s vehicle emissions control program. ICCT, Beijing, Berlin, Brussels, San Francisco, Washington.

Bernard, S.M.; Samet, J.M.; Grambsch, A.; Ebi, K.L.; Romieu, I., (2001). The potential impacts of climate variability and change on air pollution-related health effects in the United States. Environ. Health Perspect., 109(Suppl 2): 199-209 (11 pages).

Box, G.E.; Jenkins, G.M., (1976). Time series analysis: forecasting and control. Revised Ed. Holden-Day.

Brocklebank, J.C.; Dickey, D.A.; Choi, B., (2018). SAS for forecasting time series. SAS institute.

CAI-Asia Factsheet, (2010). Nitrogen dioxide (NO2): status and trends in Asia. No. 14.

Caloiero, T.; Coscarelli, R.; Ferrari, E.; Sirangelo, B., (2017). Trend analysis of monthly mean values and extreme indices of daily temperature in a region of southern Italy. Int. J. Climatol., 37(S1): 284-297 (14 pages).

Can, A., (2017). Time Series Analysis of air pollutants for Karabük province. In ITM web of conferences Vol. 9, p. 02002. EDP Sciences. AMCSE, Rome, Italy 5-7 October.

Chattopadhyay, G.; Chakraborthy, P.; Chattopadhyay, S., (2012). Mann–Kendall trend analysis of tropospheric ozone and its modeling using ARIMA. Theor. Appl. Climatol., 110(3): 321-328 (8 pages).

Chaudhuri, S.; Dutta, D., (2014). Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models. Environ. Monit. Assess., 186(8): 4719-4742 (24 pages).

Cohen, A.J.; Ross Anderson, H.; Ostro, B.; Pandey, K.D.; Krzyzanowski, M.; Künzli, N.; Gutschmidt, K., Pope, A.; Romieu, I.; Samet, J.M.; Smith, K., (2005). The global burden of disease due to outdoor air pollution. J. Toxicol. Environ. Health, Part A, 68(13-14): 1301-1307 (7 pages).

Da Silva, R.M.; Santos, C.A.; Moreira, M.; Corte-Real, J.; Silva, V.C.; Medeiros, I.C., (2015). Rainfall and river flow trends using Mann–Kendall and Sen’s slope estimator statistical tests in the Cobres river basin. Nat. Hazard., 77(2): 1205-1221 (17 pages).

Dai, Y.H.; Zhou, W.X., (2017). Temporal and spatial correlation patterns of air pollutants in Chinese cities. PloS One, 12(8): 1-24 (24 pages).

Dickey, D.A.; Fuller, W.A., 1(979). Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc., 74(366a): 427-431 (5 pages).

Drápela, K.; Drápelová, I., (2011). Application of Mann-Kendall test and the Sen's slope estimates for trend detection in deposition data from Bílý Kříž (Beskydy Mts., the Czech Republic) 1997-2010. Beskydy, 4(2): 133-146 (14 pages).

Emami, F.; Masiol, M.; Hopke, P.K., (2018). Air pollution at Rochester, NY: long-term trends and multivariate analysis of upwind SO2 source impacts. Sci. Total Environ., 612: 1506-1515 (10 pages).

Emberson, L.; Ashmore, M.; Murray, F., (2003). Air pollution impacts on crops and forests: a global assessment. Imperial College Press.

Eymen, A.; Köylü, Ü., (2018). Seasonal trend analysis and ARIMA modeling of relative humidity and wind speed time series around Yamula Dam. Meteorol. Atmos. Phys.: 1-12 (12 pages).

Gocic, M.; Trajkovic, S., (2013). Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia. Global Planet. Change, 100: 172-182 (11 pages).

Gulia, S.; Nagendra, S.S.; Khare, M.; Khanna, I., (2015). Urban air quality management-a review. Atmos. Pollut. Res., 6(2): 286-304 (19 pages).

Gurjar, B.R.; Butler, T.M.; Lawrence, M.G.; Lelieveld, J., (2008). Evaluation of emissions and air quality in megacities. Atmos. Environ., 42(7): 1593-1606 (14 pages).

Hilboll, A.; Richter, A.; Burrows, J.P., (2017). NO2 pollution over India observed from space–the impact of rapid economic growth, and a recent decline. Atmos. Chem. Phys. Discuss.

Hirsch, R.M.; Alexander, R.B.; Smith, R.A., (1991). Selection of methods for the detection and estimation of trends in water quality. Water Resour. Res., 27(5): 803-813 (11 pages).

Jaruskova, D.; Liska, I., (2011). Statistical analysis of trends in organic pollution and pollution by nutrients at selected Danube river stations. J. Environ. Monit., 13(5): 1435-1445 (11 pages).

Jayamurugan, R.; Kumaravel, B.; Palanivelraja, S.; Chockalingam, M.P., (2013). Influence of temperature, relative humidity and seasonal variability on ambient air quality in a coastal urban area. Int. J. Atmos. Sci.: (7 pages).

Junninen H.; Niska H.; Tuppurainen K.; Ruuskanen J.; Kolehmainen M., (2004). Methods for imputation of missing values in air quality data sets. Atmos. Environ., 38: 2895–2907 (13 pages).

Kampa, M.; Castanas, E., (2008). Human health effects of air pollution. Environ. Pollut., 151(2): 362-367 (5 pages).

Kendall M.G., (1975). Rank correlation methods. Griffin, London.

Kisi, O.; Ay, M., (2014). Comparison of Mann–Kendall and innovative trend method for water quality parameters of the Kizilirmak river, Turkey. J. Hydrol., 513: 362-375 (14 pages).

Kumar, D.S.; Bhushan, S.H; Kishore, D.A., (2018). Atmospheric dispersion model to predict the impact of gaseous pollutant in an industrial and mining cluster. Global J. Environ. Sci. Manage., 4(3): 351-358 (8 pages).

Lenschow, P.; Abraham, H.J.; Kutzner, K.; Lutz, M.; Preuß, J.D.; Reichenbächer, W., (2001). Some ideas about the sources of PM10. Atmos. Environ., 35: S23-S33 (11 pages).

Ma, J.; Hung, H.; Tian, C.; Kallenborn, R., (2011). Revolatilization of persistent organic pollutants in the Arctic induced by climate change. Nat. Clim. Change, 1(5): .255-260 (6 pages).

Mann, H.B., (1945). Nonparametric tests against trend. Econometrica, 13: 245–259 (15 pages).

Mayer, H., (1999). Air pollution in cities. Atmos. Environ., 33(24-25): 4029-4037 (9 pages).

National Air Quality Index, (2014). CPCB, ministry of environment, forests & climate Change, GoI.

Pandolfi, M.; Alastuey, A.; Pérez, N.; Reche, C.; Castro, I.; Shatalov, V.; Querol, X., (2016). Trends analysis of PM source contributions and chemical tracers in NE Spain during 2004–2014: a multi-exponential approach. Atmos. Chem. Phys., 16(18): 11787-11805 (19 pages).

Pascal, M.; Corso, M.; Chanel, O.; Declercq, C.; Badaloni, C.; Cesaroni, G.; Henschel, S.; Meister, K.; Haluza, D.; Martin-Olmedo, P.; Medina, S., (2013). Assessing the public health impacts of urban air pollution in 25 European cities: results of the Aphekom project. Sci. Total Environ., 449: 390-400 (11 pages).

Permissible level for pollutants, (2017). Ministry of environment, forest and climate change, GoI.

Rainfall statistics of India 2013- 2016, (2016). India Meteorological Department, Ministry of Earth Science.  

Rani, N.L.A.; Azid, A.; Khalit, S.I.; Juahir, H.; Samsudin, M.S., (2018). Air pollution index trend analysis in Malaysia, 2010-15. Pol. J. Environ. Stud., 27(2): 801–807 (7 pages).

Rodríguez, M.C.; Dupont-Courtade, L.; Oueslati, W., (2016). Air pollution and urban structure linkages: evidence from European cities. Renewable Sustainable Energy Rev., 53: 1-9 (9 pages).

Safi, M., (2016). Air quality in holy city of Varanasi 'most toxic in India'. The Guardian, Decmber 12 2016.

Sen, P.K., (1968). Estimates of the regression coefficient based on Kendall's tau. J. Am. Stat. Assoc., 63(324): 1379-1389 (11 pages).

Shukla, J.B.; Misra, A.K.; Sundar, S.; Naresh, R., (2008). Effect of rain on removal of a gaseous pollutant and two different particulate matters from the atmosphere of a city. Math. Comput. Modell., 48(5-6): 832-844 (13 pages).

Theil, H., (1950). A rank-invariant method of linear and polynomial regression analysis I II III, Nederlandse Akademie Wetenschappen Proc. Series A 53:386–392 (Part I), 521–525 (Part II), 1397–1412 (Part III) (16 pages).

Vanguelova, E.I.; Benham, S.; Pitman, R.; Moffat, A.J.; Broadmeadow, M.; Nisbet, T.; Durrant, D.; Barsoum, N.; Wilkinson, M.; Bochereau, F.; Hutchings, T., (2010). Chemical fluxes in time through forest ecosystems in the UK–soil response to pollution recovery. Environ. Pollut., 158(5): 1857-1869 (23 pages).

Watthanacheewakul, L., (2011). Comparisons of power of parametric and nonparametric test for testing means of several Weibull populations. In Proceedings of the International Multi-Conference of Engineers and Computer Scientist. Hong Kong, 16-18 March.

Xu, Z.X.; Chen, Y.N.; Li, J.Y., (2004). Impact of climate change on water resources in the Tarim river basin. Water Resour. Manage., 18(5): 439-458 (20 pages).

Zuma-Netshiukhwi, G.; Stigter, K.; Walker, S., (2013). Use of traditional weather/climate knowledge by farmers in the South-western free state of South Africa: Agrometeorological learning by scientists. Atmos., 4(4): 383-410 (28 pages).

 

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