Evaluating the Performance of Regional and Global Forecasting Models for Accurate PM <sub>2.5</sub> 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.
Authors
- Pawan Gupta (ORCID: https://orcid.org/0000-0002-0979-472X)
- Rajesh Kumar (ORCID: https://orcid.org/0000-0002-3135-9556)
- Chinmay Jena (ORCID: https://orcid.org/0000-0002-0172-4954)
- Shweta Bhati (ORCID: https://orcid.org/0000-0002-5475-7565)
- Partha S. Bhattacharjee (ORCID: https://orcid.org/0000-0003-1117-0649)
- A. Jayakumar
- Kamaljit Ray (ORCID: https://orcid.org/0000-0002-2781-6857)
- Vijay Kumar Soni (ORCID: https://orcid.org/0000-0002-3880-3613)
- Alqamah Sayeed (ORCID: https://orcid.org/0000-0001-6898-8148)
- Gayatri Kalita (ORCID: https://orcid.org/0000-0002-5624-1247)
- T. J. Anurose
- Sreyashi Debnath (ORCID: https://orcid.org/0000-0001-5144-8807)
- Gaurav Rajabhau Govardhan (ORCID: https://orcid.org/0000-0003-0213-6289)
- Prafull P. Yadav (ORCID: https://orcid.org/0009-0007-2995-5117)
- Johannes Flemming (ORCID: https://orcid.org/0000-0003-4880-5329)
- Junhyeon Seo (ORCID: https://orcid.org/0000-0003-2416-6382)
- Rajmal Jat (ORCID: https://orcid.org/0000-0002-3611-0637)
- Sachin Dinkar Ghude (ORCID: https://orcid.org/0000-0002-4530-2059)
- Sudhir Kumar Atri (ORCID: https://orcid.org/0009-0007-5863-2392)
Institutions
- NSF National Center for Atmospheric Research (US)
- Goddard Space Flight Center (US)
- U.S. National Science Foundation (US)
- National Centre for Medium Range Weather Forecasting (IN)
- Indian Institute of Tropical Meteorology (IN)
- European Centre for Medium-Range Weather Forecasts (GB)
- University Grants Commission (IN)
- India Meteorological Department (IN)
- Ministry of Earth Sciences (IN)
- Morgan State University (US)
- Savitribai Phule Pune University (IN)
- Science Applications International Corporation (United States) (US)
Publication Details
- Journal
- Journal of Geophysical Research Atmospheres
- Published
- 2025-10-09
- DOI
- https://doi.org/10.1029/2025jd043719
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.00