Evaluating the Performance of Regional and Global Forecasting Models for Accurate PM2.5 Prediction and Air Quality Index Assessment in Delhi, India

Abstract

Accurate forecasting of PM_2.5 (particulate matter ≤2.5 μm) is essential for effective air quality management, particularly in urban areas such as Delhi, which frequently experience severe pollution episodes. This study evaluates the predictive capabilities of regional and global forecasting models for PM_2.5 concentrations and the associated Air Quality Index (AQI) in Delhi, India. A multi-model assessment was conducted using three regional models (WRF-Chem, SILAM, and DM-Chem) and four global models (IFS, GEOS-FP, GEFS-Aerosols, and the machine learning-based GEOS-ML). Forecasts from these models were validated against hourly in situ measurements from 39 Central Pollution Control Board (CPCB) stations in Delhi. Results revealed that the Air Quality Early Warning System (AQEWS) based on WRF-Chem exhibited the highest predictive accuracy (Performance Index, PI = 87), with minimal deviations from observations. The GEOS-ML model (PI = 70) effectively captured key variations using a machine learning approach. DM-Chem (330 m: PI = 69, 1.5 km: PI = 61) showed reasonable agreement, whereas IFS (PI = 60), GEOS-FP (PI = 52), and GEFS-Aerosols (PI = 47) captured broader trends with varying accuracy. SILAM (PI = 58) exhibited notable discrepancies during high-pollution events. This study underscores the need for rigorous evaluation of forecasting systems to enhance air quality prediction in polluted urban environments such as Delhi. Identifying the most reliable models supports data-driven decision-making for air pollution mitigation and public health protection.