Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …

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

Annual global satellite-based estimates of fine particulate matter (PM2. 5) are widely relied
upon for air-quality assessment. Here, we develop and apply a methodology for monthly …

The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …

The complexity and dynamics of the environment make it extremely difficult to directly predict
and trace the temporal and spatial changes in pollution. In the past decade, the …

Smoke from wildfires is a growing health risk across the US. Understanding the spatial and
temporal patterns of such exposure and its population health impacts requires separating …

The heat increase caused by climate change has worsened the urban heat environment and
damaged human health, which has led to heat-related mortality. One of the most important …

Various approaches have been proposed to model PM 2.5 in the recent decade, with
satellite-derived aerosol optical depth, land-use variables, chemical transport model …

Fine particulate matter with aerodynamic diameters≤ 2.5 µ m (PM 2.5) has adverse effects
on human health and the atmospheric environment. The estimation of surface PM 2.5 …

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 …