Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …

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

Gaseous pollutants at the ground level seriously threaten the urban air quality environment
and public health. There are few estimates of gaseous pollutants that are spatially and …

Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality
and public health. This study developed a novel, artificial intelligence approach by …

Ozone (O 3) is an important trace and greenhouse gas in the atmosphere, posing a threat to
the ecological environment and human health at the ground level. Large-scale and long …

Fine particulate matter (PM2. 5) chemical composition has strong and diverse impacts on the
planetary environment, climate, and health. These effects are still not well understood due to …

Exposure to fine particulate matter (PM 2.5) can significantly harm human health and
increase the risk of death. Satellite remote sensing allows for generating spatially …

Air pollution has altered the Earth's radiation balance, disturbed the ecosystem, and
increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air …

Background Long-term improvements in air quality and public health in the continental USA
were disrupted over the past decade by increased fire emissions that potentially offset the …

Respirable particles with aerodynamic diameters≤ 10 µm (PM 10) have important impacts
on the atmospheric environment and human health. Available PM 10 datasets have coarse …