Abstract Machine learning models based on satellite remote sensing have gained
widespread use in estimating ground-level air pollutant concentrations, which overcome the …

Satellite data are vital for understanding the large-scale spatial distribution of particulate
matter (PM2. 5) due to their low cost, wide coverage, and all-weather capability. Estimation …

The observation-based air pollution forecasting method has high computational efficiency
over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to …

Rapid urbanization and industrialization have intensified air pollution, posing severe health
risks and necessitating accurate PM 2.5 predictions for effective urban air quality …

Dust pollution is a major threat to human health and ecosystems, highly affecting both the
outdoor and indoor air quality, while dust particles are an important carrier of potential toxic …

This study estimates intra-daily PM10 concentrations at 213 inland and coastal monitoring
sites in Türkiye from 2008 to 2019 using satellite-based aerosol optical depth (AOD) from the …

Most researchers are beginning to appreciate the use of remote sensing satellites to assess
PM2. 5 levels and use machine learning algorithms to automate the collection, make sense …

Continuous and accurate surface pollutant data can provide data support for health effect
analysis. Based on the hourly AOD data of the Himawari-8 satellite as the basic data set, this …

In China, population growth and aging have partially negated the public health benefits of
air pollution control measures, underscoring the ongoing need for precise PM 2.5 monitoring …

Aerosol optical depth (AOD) is an essential metric for evaluating the atmospheric aerosol
load and its impacts on climate, air quality, and public health. In this study, the AOD data …