Main Article Content


Recently, trend detection in ambient air pollutants has received a lot of interest, particularly in relation to climatic changes. Air pollutants data that were acquired from monitoring stations from 2015 to 2021 were used in the current investigation. The direction and size of the monotonic trend were determined using the Mann-Kendall test and Sen's slope estimator. The findings showed that there was significant fluctuation in different parameters over time. According to the study, SO2 and NO2 indicate a slightly increasing tendency with approximate annual concentrations of 6mg/m3 and 40mg/m3, respectively, whereas PM2.5 shows a decreasing trend with an approximate annual concentration of 130mg/m3. For all of Odisha's districts, PM10 exhibits no trend, with annual concentrations of about 90mg/m3. The study found that while NO2, PM2.5, and PM10 concentrations were significantly over the standard allowed limits while SO2 concentrations were significantly below them. Specific actions are needed to reduce these pollutants' emissions in Odisha.


Air Quality NO2 Particulate matter SO2 Trend analysis

Article Details

How to Cite
T., G., K., S. K., & Bhattacharyya, B. (2023). Detecting air pollutants trends using Mann-Kendall tests and Sen’s slope estimates. Environment Conservation Journal, 24(3), 157–166.


  1. Ahamad, F. Bhutiani, R. & Ruhela, M. (2022). Environmental Quality Monitoring Using Environmental Quality Indices (EQI), Geographic Information System (GIS), and Remote Sensing: A Review. GIScience for the Sustainable Management of Water Resources, 331. (Chapter number-18, pp.331-348, ISBN ebook: 9781003284512). DOI:
  2. Anonymous (2019). Poison in the Air: Six Odisha Cities most polluted among 102 cities in India. [].
  3. Bansal, G., Bandivadekar, A., (2013). Overview of India’s vehicle emissions control program. ICCT, Beijing, Berlin, Brussels, San Francisco, Washington.
  4. Bhaskar, V. S., & Sharma, M. (2008). Assessment of fugitive road dust emissions in Kanpur, India: A note. Transportation Research Part D: Transport and Environment, 13(6), 400-403. DOI:
  5. Bhutiani, R., Kulkarni, D. B., Khanna, D. R., Tyagi, V., & Ahamad, F. (2021). Spatial and seasonal variations in particulate matter and gaseous pollutants around integrated industrial estate (IIE), SIDCUL, Haridwar: a case study. Environment, Development and Sustainability, 23(10), 15619-15638. DOI:
  6. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  7. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press.
  8. Chattopadhyay, G., Chakraborthy, P., & Chattopadhyay, S. (2012). Mann–Kendall trend analysis of tropospheric ozone and its modeling using ARIMA. Theoretical and Applied Climatology, 110(3), 321-328. DOI:
  9. 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. Environmental monitoring and assessment, 186(8), 4719-4742. DOI:
  10. Chelani, A. B., Gajghate, D. G., & Hasan, M. Z. (2002). Prediction of ambient PM10 and toxic metals using artificial neural networks. Journal of the Air & Waste Management Association, 52(7), 805-810. DOI:
  11. Das, N., Sutradhar, S., Ghosh, R., & Mondal, P. (2021). Asymmetric nexus between air quality index and nationwide lockdown for COVID-19 pandemic in a part of Kolkata metropolitan, India. Urban Climate, 36, 100789. DOI:
  12. 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. Natural Hazards, 77(2), 1205-1221. DOI:
  13. Dey, S., Di Girolamo, L., van Donkelaar, A., Tripathi, S. N., Gupta, T., & Mohan, M. (2012). Variability of outdoor fine particulate (PM2. 5) concentration in the Indian Subcontinent: A remote sensing approach. Remote sensing of environment, 127, 153-161. DOI:
  14. Eymen, A., & Köylü, Ü. (2019). Seasonal trend analysis and ARIMA modeling of relative humidity and wind speed time series around Yamula Dam. Meteorology and Atmospheric Physics, 131(3), 601-612. DOI:
  15. Gupta, G. P., Kumar, B., Singh, S., & Kulshrestha, U. C. (2016). Deposition and impact of urban atmospheric dust on two medicinal plants during different seasons in NCR Delhi. Aerosol and Air Quality Research, 16(11), 2920-2932. DOI:
  16. Gowthaman, T., Kumar, K. S., Adarsh, V. S., & Bhattacharyya, B. (2022). Trend Analysis and ARIMA Models for Water Quality Parameters of Brahmani River, Odisha, India. Int. J. Environ. Clim. Change, 12(12), 219-228. DOI:
  17. 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. Atmospheric Chemistry and Physics Discussions, 1-18. DOI:
  18. Jaiswal, A., Samuel, C., & Kadabgaon, V. M. (2018). Statistical trend analysis and forecast modeling of air pollutants. Global Journal of Environmental Science and Management, 4(4), 427-438.
  19. Karpouzos, D. K., Kavalieratou, S., & Babajimopoulos, C. (2010). Trend analysis of precipitation data in Pieria Region (Greece). European Water, 30(30), 30-40.
  20. Kendall, M. G. (1975). Rank correlation methods. Griffin, London.
  21. 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 Journal of Environmental Science and Management, 4(3), 351-358.
  22. Lenschow, P., Abraham, H. J., Kutzner, K., Lutz, M., Preuß, J. D., & Reichenbächer, W. (2001). Some ideas about the sources of PM10. Atmospheric Environment, 35, S23-S33. DOI:
  23. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259. DOI:
  24. Mohapatra, K. & Biswal, K. S. (2014). Assessment of Ambient Air Quality Index (AQI) In Bhubaneswar, the Capital City of Odisha. International Journal of Advance Research in Science and Engineering, 3(6), 190-196.
  25. National Ambient Air Quality Status & Trends. In India-2010, NAAQMS/ 35/2011-2012, CPCB.
  26. National Air Quality Index (2014). CPCB, Ministry of environment, forests & climate Change, GoI.
  27. Pal, R., Chowdhury, S., Dey, S., & Sharma, A. R. (2018). 18-Year Ambient PM2.5 Exposure and Night Light Trends in Indian Cities: Vulnerability Assessment. Aerosol and Air Quality Research, 18: 2332–2342. DOI:
  28. Permissible level for pollutants (2017). Ministry of environment, forest and climate change, GoI.
  29. Ruhela, M., Sharma, K., Bhutiani, R., Chandniha, S. K., Kumar, V., Tyagi, K., ... & Tyagi, I. (2022a). GIS-based impact assessment and spatial distribution of air and water pollutants in mining area. Environmental Science and Pollution Research, 1-15. DOI:
  30. Ruhela, M., Maheshwari, V., Ahamad, F., & Kamboj, V. (2022b). Air quality assessment of Jaipur city Rajasthan after the COVID-19 lockdown. Spatial Information Research, 30(5), 597-605 DOI:
  31. Samal, K. K. R., Babu, K. S., Das, S. K., & Acharaya, A. (2019, August). Time series based air pollution forecasting using SARIMA and prophet model. In proceedings of the 2019 international conference on information technology and computer communications (pp. 80-85). DOI:
  32. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389. DOI:
  33. Sharma, B., Vaish, B., Srivastava, V., Singh, S., Singh, P., & Singh, R. P. (2018). An insight to atmospheric pollution-improper waste management and climate change nexus. In Modern age environmental problems and their remediation (pp. 23-47). Springer, Cham. DOI:
  34. Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis. Indagationes mathematicae, 12(85), 173.
  35. World Health Organisation. (2010). Quantifying the Burden of Disease from Mortality and Morbidity‖. World Health Organisation.