ECOLOGICAL ANALYSIS OF AIR POLLUTION AND RESPIRATORY MORBIDITY USING PUBLIC HEALTH SURVEILLANCE DATA IN MAJOR INDIAN CITIES

Ambient air pollution has become a grave urban health crisis in India, with major cities experiencing recurrent spikes in fine particulate concentrations that correlate with increased respiratory illness. Across five metropolitan centers—Delhi, Mumbai, Kolkata, Bengaluru, and Chennai—this study explored the ecological association between monthly average pollutant levels (PM₂.₅, PM₁₀, NO₂, SO₂, O₃) and surveillance-based respiratory morbidity (ARI, ILI, SARI, asthma, COPD). We employed an ecological, cross-sectional time-trend design using publicly available data from CPCB and IDSP, along with meteorological covariates from IMD, spanning 2019–2024. Descriptive statistics revealed stark inter-city differences: Delhi exhibited the highest pollutant load and morbidity burden, especially in winter and post-Diwali months, while coastal cities such as Chennai showed lower but seasonally elevated levels. Correlation analysis consistently showed strong positive relationships between PM₂.₅ and morbidity counts (r ≈ 0.58–0.75, p < 0.01), with slightly lower but significant associations for PM₁₀. Multivariate regression models, adjusted for weather and population size, identified PM₂.₅ as the dominant predictor (β ≈ 0.03 cases / µg/m³; p < 0.001), explaining up to 72% of the variance in respiratory case trends in Delhi and about 60% across other cities. Lag analysis indicated delayed morbidity spikes 2–3 weeks following sustained pollution episodes. These results align with ecological assessments from cities like Delhi, Lucknow, and Maharashtra, and reflect the short-term health improvements observed during COVID-19 lockdown phases. The study underscores the value of ecological surveillance as a tool for public health planning, and highlights the urgent need for integrated pollutant-health monitoring, early warning systems, and AI/ML-assisted forecasting for city-level morbidity in urban India.