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

Air pollution is the one of the most significant environmental risks to health worldwide. An
accurate assessment of population exposure would require a continuous distribution of …

Seamless hourly surface fine particulate matters (PM 2.5) datasets are urgently needed for
environmental and epidemiologic studies. Most existing models rely on satellite AOD with …

Dealing nuclear wastewater into the sea significantly threatens the global ecological
environment and public health. Existing research primarily focuses on the roles of emission …

With the potential to cause millions of deaths, PM 2.5 pollution has become a global
concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM 2.5 …

The latest epidemiological studies have revealed that the adverse health effects of PM2. 5
have impacts beyond respiratory and cardio-vascular diseases and also affect the …

Air pollution is considered one of the major environmental risks to health worldwide.
Researchers are making significant efforts to study it, thanks to state-of-art technologies in …

Robust high spatiotemporal resolution daily PM2. 5 exposure estimates are limited in
Australia. Estimates of daily PM2. 5 and the PM2. 5 component from extreme pollution …

With the development of industry, the issue of air pollution is of great concern. Due to the
sparsity of monitoring stations, acquiring full-coverage PM2. 5 concentration remains …

By utilizing top-of-atmosphere radiation (TOAR) data from China's new generation of
geostationary satellites (FY-4A and FY-4B) along with interpretable machine learning …