TY - M-10515
AU - Amlani, Dr. Khimya
TI - Statistical Analysis of Air Quality Impact on Respiratory Disease Prevalence
T2 - Scientific Research Journal of Science, Engineering and Technology

PY - 2026
VL - 4
IS - 1
SN - 2584-0584

AB - 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.

KW - PM₂.₅
KW -  respiratory disease
KW -  multiple linear regression
KW -  logistic regression
KW -  ANOVA
DO -