A Framework for Air Quality Intelligence and Decision Support in India

Sanjeevan represents a deliberately modest but practically motivated contribution to the problem of air quality decision support in urban India. Its central argument is that the gap between AQI data availability and actionable citizen guid-ance is not primarily a sensing problem-it is an intelligence and interface design problem. Existing platforms produce accurate measurements but do not reason about them; San-jeevan demonstrates that even lightweight rule-based rea-soning, applied systematically to publicly available data, can meaningfully enrich the interpretive layer between raw AQI values and real-world decisions. The system’s strengths lie in its explainability-first de-sign, its zero-dependency deployability on commodity mo-bile hardware, and its dual-user architecture that serves both individual citizens and institutional stakeholders within a unified data pipeline. Its limitations-particularly the heuris-tic nature of its source attribution, the incompleteness of its policy dashboard, and the absence of formal validation-are acknowledged without reservation. These limitations de-fine the boundary between prototype and production system; crossing that boundary is the work of the research trajectory that this paper initiates. Air pollution in India will not be resolved by a mo-bile application. But the translation of scientific monitor-ing data into intelligible, actionable, and personalized guid-ance is a necessary condition for informed public and policy response-and that translation is precisely the function that systems like Sanjeevan are designed to perform.  

[1]    World Health Organization, “WHO Global Air Qual-ity Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide,” WHO Press, Geneva, 2021. [2]    J. Lelieveld, J. S. Evans, M. Fnais, D. Giannadaki, and A. Pozzer, “The contribution of outdoor air pollution sources to premature mortality on a global scale,” Na-ture, vol. 525, no. 7569, pp. 367–371, Sep. 2015. [3]    SAFAR (System of Air Quality and Weather Fore-casting and Research), Ministry of Earth Sciences, Government of India. [Online]. Available: https: //safar.tropmet.res.in [4]    J. H. Seinfeld and S. N. Pandis, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 3rd ed. Hoboken, NJ: Wiley, 2016. [5]    D. Byun and K. L. Schere, “Review of the governing equations, computational algorithms, and other com-ponents of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system,” Applied Mechan-ics Reviews, vol. 59, no. 2, pp. 51–77, Mar. 2006. [6]    Y. Zheng, F. Liu, and H.-P. Hsieh, “U-Air: When ur-ban air quality inference meets big data,” in Proc. 19th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), Chicago, IL, USA, pp. 1436–1444, Aug. 2013. [7]    B. S. Freeman, G. Taylor, B. Gharabaghi, and J. The´, “Forecasting air quality time series using deep learn-ing,” Journal of the Air & Waste Management Associ-ation, vol. 68, no. 8, pp. 866–886, 2018. [8]    L. Bai, J. Wang, X. Ma, and H. Lu, “Air pollution fore-casts: An overview,” International Journal of Environ-mental Research and Public Health, vol. 16, no. 19, p. 3729, Oct. 2019. [9]    S. Gupta, P. Choudhary, A. Kumar, J. George, T. Ma-jumdar, and S. Hazarika, “Source apportionment of PM2.5 in Delhi: An overview,” Aerosol and Air Qual-ity Research, vol. 17, no. 10, pp. 2590–2607, 2017. [10]    S. K. Guttikunda and P. Gurjar, “Role of meteorology, emission sources, and urban activity patterns on air quality of a polluted Indian megacity,” Environmental Monitoring and Assessment, vol. 184, no. 4, pp. 2277– 2295, 2012.