A review of extreme air pollution measurement and modeling techniques with applications

Abstract

Air pollution remains a major global environmental and public health challenge, with extreme air pollution events posing severe risks to human health, ecosystems, and socio-economic stability. This review provides a comprehensive synthesis of recent advances in the monitoring, modeling, and assessment of extreme air pollution events. A systematic survey of peer-reviewed literature (2005–2025) is conducted to evaluate ground-based regulatory monitoring, advanced in situ instrumentation, satellite and ground-based remote sensing, emerging low-cost and IoT-enabled sensor systems, and a wide spectrum of deterministic, statistical, machine learning (ML), and hybrid modeling approaches. The analysis shows that conventional monitoring systems, while highly accurate, remain limited in spatial and temporal resolution for capturing rapidly evolving and heterogeneous extreme events, particularly in data-sparse regions. Satellite observations and mobile sensing technologies significantly enhanced spatial coverage, whereas machine learning and hybrid frameworks short-term prediction capability. However, key challenges persist, including uncertainties in chemical transport models, limited interpretability of machine learning models, sensor calibration inconsistencies, and constraints in data availability and computational efficiency. Evidence from severe pollution events in Beijing (2013–2017) and Delhi (2016–2019) highlights both technological progress and enduring gaps in real-time monitoring, forecasting, and policy responsiveness. The review identified integrated monitoring–modeling frameworks, multi-platform data fusion, and uncertainty-aware approaches as critical pathways for improving predictive reliability and decision support. These advancements, coupled with clean energy transitions and policy integration, are essential for developing resilient, scalable, and sustainable air quality management systems. © 2026 Elsevier Ltd