Evaluating the Performance of Regional and Global Forecasting Models for Accurate PM 2.5 Prediction and Air Quality Index Assessment in Delhi, India

作者：Prafull P. Yadav, Rajmal Jat, Sachin D. Ghude, Gaurav Govardhan, Rajesh Kumar, Sreyashi Debnath, Gayatri Kalita, Chinmay Jena, Vijay Kumar Soni, A. Jayakumar, T. J. Anurose, Shweta Bhati, Alqamah Sayeed, Junhyeon Seo, Pawan Gupta, Partha S. Bhattacharjee, Johannes Flemming, Kamaljit Ray, Sudhir Kumar Atri · 发表于：Journal of Geophysical Research Atmospheres · 年份：2025 · DOI：10.1029/2025jd043719 · 被引用次数：3 · 研究领域：Air Quality Monitoring and Forecasting、Air Quality and Health Impacts、Atmospheric chemistry and aerosols

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 support...