Assessment of Air Quality and Health Impacts Using Multi-Parameter Environmental Data in Urban Areas

Résumé

Urban air pollution remains one of the leading environmental risk factors for morbidity and mortality worldwide, yet single-pollutant assessments often understate the true public health burden because ambient exposure is inherently multi-dimensional. This study presents an integrated, multi-parameter framework for assessing urban air quality and its associated health impacts by jointly analysing criteria air pollutants (PM2.5, PM10, NO2, SO2, CO, O3), meteorological covariates (temperature, relative humidity, wind speed, atmospheric pressure, boundary-layer height), and health outcome indicators (respiratory and cardiovascular hospital admissions and emergency visits). A one-year continuous monitoring design across five representative urban microenvironments (traffic-dominated, industrial, residential, commercial, and background/control sites) was used to construct a composite Air Quality Index (AQI), characterise pollutant covariation and diurnal/seasonal dynamics, and quantify exposure-response relationships using generalised additive models (GAMs) with distributed-lag non-linear terms to account for delayed health effects. Across the illustrative dataset used to demonstrate the framework, particulate matter and NO2 showed the strongest and most temporally consistent associations with respiratory and cardiovascular outcomes, meteorological variables substantially modified pollutant dispersion and apparent health risk, and traffic-dominated sites consistently exceeded WHO Air Quality Guideline levels. The results underscore the value of multi-parameter, spatially resolved monitoring combined with time-series epidemiological modelling for evidence-based urban air quality management. The proposed framework is generalisable to other cities and can support targeted interventions, early-warning systems, and health-protective urban planning.

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