Dear Colleagues,

The World Health Organization (WHO) revised the air quality guidelines on September 22, 2021, and strengthened the standards for pollutants such as PM2.5, PM10, NO₂ and CO in comparison with the 2005 guidelines. While ‘nearly 80% of PM2.5-related preventable deaths could be avoided if the world met the new guidelines’, 50% of European cities and 38% of US cities failed to meet the air quality guidelines for PM2.5. To meet these guidelines, China faces the challenge of reducing PM2.5 exposure to its citizens to one-third of the current level. Similar issues exist in other parts of the world.

To improve human wellbeing, the past decade has seen efforts from both academia and industry in large-scale spatial–temporal air quality data accumulation and the development of advanced machine learning models to process data for air quality monitoring and forecasting. However, works that comprehensively assess model performance are still lacking, especially those operating in the presence of data and model parameter uncertainties, etc.

The aim of this Special Issue, hosted by the open access journal Atmosphere, is to promote recent advances in air quality monitoring and forecasting techniques. The topics cover a range of research topics, including but not limited to:

• air quality models – for indoor and outdoor environments;
• high-resolution sensors for monitoring and modelling air quality data;
• methods for prediction and assessment of air quality;
• scalable and distributed machine learning models in large-scale spatial–temporal air quality forecasting;
• machine learning models for air quality data and mobility data association;
• machine learning models for air quality cleaning and outlier detection;
• machine learning solutions for low-cost air quality sensors, etc.;
• machine learning solutions for urban and rural area air quality monitoring;
• other related sub-areas.

Dr. Peng Wang
Prof. Dr. Lyudmila Mihaylova
Dr. Khan Alam
Prof. Dr. Muhammad Fahim Khokhar
Prof. Dr. Liangxiu Han
Dr. Yaxing Du
Guest Editors

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• air quality forecasting
• machine learning for air quality
• air quality modeling
• air quality assessment
• urban air quality
• indoor and outdoor air quality
• aerosol monitoring
• distributed system for air quality
• sensors for air quality
• air quality model assessment