Markov chain modeling of Air Quality Index transitions in Mumbai metropolitan region during 2024 – 2025
DOI:
https://doi.org/10.60923/issn.2281-4485/26111Keywords:
AQI, Air Quality, Markov Chain, Mumbai Metropolitan Region, SDGsAbstract
Air pollution always remains as the major environmental and public health threat, rapidly increasing in many urban areas. The accurate management of Air Quality Index (AQI) is essential for effective environmental protection and policy intervention. This study applied discrete time Markov chain model to investigate the transition behaviour of AQI state based on health standards like Good, Satisfactory, Moderate, and Poor across different monitoring stations in the Mumbai Metropolitan Region (MMR) during 2024 – 2025. The transition probabilities were used to understand the persistence, deterioration and recovery patterns of air quality. The study reported that the steady state analysis indicated that Moderate air quality remains persistent in long run condition across monitoring stations. The mean recurrence time indicated that expected number of days required for an AQI category to revisit after leaving that state. It was observed that on an average, the mean return time of Good state was decreased from 2024 to 2025, suggesting favourable air quality recurring more frequently in 2025. However, the comparison between 2024 and 2025 reveals slight improvement in air quality during 2025, characterized by reduced persistence rate of the poor category, longer mean recurrence rate for poor state of pollution and decreased mean transitions from Good to Poor states. Overall, the Markov Chain analysis highlighted a slight improvement in air quality from 2024 to 2025 across the MMR. The results demonstrated that Markov Chain modeling provides valuable insights in developing study specific threshold ranges, offering a statistically robust decision support framework for early intervention strategies and timely implementation of the Graded Response Action Plan (GRAP). Such proposed threshold ranges of AQI can support the achievement of Sustainable Development Goals (SDGs), particularly those related to sustainable urban planning, by concentrating on reduced vehicular emissions and improving overall air quality.
References
CARAKA R.E., CHEN R.C., TOHARUDIN T., PARDAMEAN B., YASIN H., WU S.H. (2019) Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and Hybrid Var-Nn-Pso. IEEE Access, 7, 161654–161665. https://doi.org/10.1109/ACCESS.2019.2950439
ELGHARBI S., ESGHIR M., IBRIHICH O., ABARDA A., EL HAJJI S., ELBERNOUSSI S. (2020) Grey-Markov Model for the Prediction of the Electricity Production and Consumption. In Y. Far-haoui (Ed.), Big Data and Networks Technologies, 81:206–219). Springer International Publi-shing. https://doi.org/10.1007/978-3-030-23672-4_16
FINAL REPORT-MUMBAI. (2023) Air quality monitoring, emission inventory and source appor-tionment studies for ten cities in the state of Maharashtra. https://www.mpcb.gov. in/sites/default/files/inlinefiles/Final_SA%20%26%20EI%20Report_Mumbai.pdf
GRADED RESPONSE ACTION PLAN (GRAP) FOR NCR. (2024) Commission of Air Quality Management in National Capital Region and adjoining areas.
HOLMES J., HASSINI S. (2021) Discrete-Time Markov Chain Modelling of the Ontario Air Qual-ity Health Index. Water, Air and Soil Pollution, 232(4): 158. https://doi.org/10.1007/s11270-021-05096-1
JALE J.D.S., XAVIER JÚNIOR S.F.A., XAVIER É.F.M., STOŠIĆ T., STOŠIĆ B., FERREIRA T.A.E. (2019) Application of Markov chain on daily rainfall data in Paraíba-Brazil from 1995-2015. Acta Scientiarum. Technology, 41(1): 37186. https://doi.org/10.4025/actascitechnol.v41i1.37186
LIU Y., WEN L., LIN Z., XU C., CHEN Y., LI Y. (2024) Air quality historical correlation model based on time series. Scientific Reports, 14(1): 22791. https://doi.org/10.1038/s41598-024-74246-2
MASSERAN N. (2015). Markov Chain model for the stochastic behaviors of wind-direction data. Energy Conversion and Management, 92:266–274. https://doi.org/10.1016/j.enconman.2014.12.045
MASOUDI M., BEHZADI F., SAKHAEI M. (2018) Status and prediction of sulfur dioxide as an air pollutant in Tehran, Iran. EQA - International Journal of Environmental Quality, 28:13-23. https://doi.org/10.6092/ISSN.2281-4485/7420
MASOUDI M., SAKHAEI M., BEHZADI F. (2020) Status of CO as an air pollutant and its predic-tion using meteoro-logical parameters in Tehran, Iran. EQA - International Journal of Environ-mental Quality, 37:31-39. https://doi.org/10.6092/ISSN.2281-4485/9369
MEDHI J. (2012). Stochastic processes (Third edition). New Academic Science.
MOHAMAD N.S., DENI S.M., UL-SAUFIE A.Z. (2018) Application of the First Order of Markov Chain Model in Describing the PM10 Occurrences in Shah Alam and Jerantut, Malaysia. Pertanika Journal of Science and Technology, 26(1):367 - 378.
NEBENZAL A., FISHBAIN B. (2018) Long-term fore-casting of nitrogen dioxide ambient levels in metropolitan areas using the discrete-time Markov model. Environmental Modelling and Soft-ware, 107:175–185. https://doi.org/10.1016/j.envsoft.2018.06.001
PRASAD S.V.S, NAMBOODIRI V.V. (2020) Predicting the Air Quality Index of Industrial Areas in an Industrialized City in India Using Adopting Markov Chain Model. Journal of Environmental Health and Sustainable Development. https://doi.org/10.18502/jehsd.v5i4.4962
RODRIGUES E.R., TARUMOTO M.H., TZINTZUN G. (2019) Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data. Journal of Applied Statistics, 46(3): 395–415. https://doi.org/10.1080/02664763.2018.1492527
ROSS SHELDON M. (1995). Stochastic Processes, Second Edition (Reference book), ISBN 0-471-12062-6.
SAHIN A.D., SEN Z. (2001) First-order Markov chain approach to wind speed modelling, Journal of Wind Engineering and Industrial Aerodynamics, 89(4):263-269. https://doi.org/10.1016/S0167-6105(00)00081-7
SHAH D.P., PATEL P. (2021) A Review of Chronological Evolution of Air Quality Indexing Sys-tems (1966 To 2021). Current World Environment, 16(3):704–725. https://doi.org/10.12944/CWE.16.3.5
SHINDE R.L., CHAVHAN R.N. (2018) Markov chain modeling of stochastic process defined on daily minimum temperature and its applications In Weather Based Crop Insurance Scheme for Banana. MAUSAM, 69(1):156–160. https://doi.org/10.54302/Mausam.V69i1.251
SDG INDICATOR 3.9.1 (2025) Mortality rate attributed to household and ambient air pollution (per 100000 popula-tion,age-standardized). https://www.who.int/data/themes/topics/indicator-groups/indicator-group-details/GHO/indicator-3.9.1-mortality-rate-attributed-to-household-and-ambi ent-air-pollution
SURVEY OF INDIA (2025) Maharashtra State Map. https://surveyofindia.gov.in/UserFiles/files/MAHARASTRA_ENG_1ST_1M_2025.pdf
SZYSZKOWICZ M. (2019) The Air Quality Health Index and all emergency department visits. Environmental Science and Pollution Research, 26(24): 24357–24361. https://doi.org/10.1007/s11356-019-05741-7
ZAKARIA N.N., OTHMAN M., SOKKALINGAM R., DAUD H., ABDULLAH L., ABDUL KA-DIR E. (2019) Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak. Sustainability, 11(19):5190. https://doi.org/10.3390/su11195190
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