India’s deteriorating urban air quality has generated a mounting public health emergency that demands rigorous quantitative analysis. This paper reports findings from a five-year, multi-city epidemiological study examining how ambient air pollutants—principally fine particulate matter (PM₂.₅)—relate to respiratory disease burden across Mumbai, Delhi, Kolkata, Chennai, and Bangalore between 2020 and 2025. Four complementary statistical methods are applied: multiple linear regression (MLR), polynomial regression, binary logistic regression, and two-way analysis of variance (ANOVA) with post-hoc comparisons. A stepwise variable selection procedure, validated through ten-fold cross-validation, further refines the predictor set. The central empirical finding is a statistically robust non-linear threshold in the PM₂.₅–respiratory admission relationship, confirmed at approximately 59.8 μg/m³ (95% CI: 56.2–63.4 μg/m³) through segmented regression, the point beyond which hospital admissions rise at an accelerating rate (β = 2.31, p < 0.001). Logistic regression further shows that each 10 μg/m³ increment in PM₂.₅ is associated with 48% higher odds of chronic respiratory disease (OR = 1.48; AUC = 0.88). Scenario modelling projects that a sustained 30% reduction in PM₂.₅ could be associated with approximately 52,000 fewer premature deaths annually. The results carry direct implications for India’s National Clean Air Programme (NCAP) and city-level clinical resource planning.
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No conflicts of interest are reported by the authors.
The author takes full responsibility for the entire study process, including design, data collection, analysis, and manuscript writing.
The research, authorship, and publication of this article were not funded by any specific grants from public, commercial, or non-profit agencies.
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I extend my gratitude to everyone who contributed their expertise to this study and manuscript, and to the anonymous reviewers for their helpful comments.
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