Climate change and rapid urbanisation exacerbated multiple urban issues threatening
urban sustainability. Numerous studies integrated machine learning and remote sensing to …

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

Abstract In East Asia, air quality has been recognized as an important public health problem.
In particular, the surface concentrations of air pollutants are closely related to human life …

Abstract Background Spatial linear Land-Use Regression (LUR) is commonly used for long-
term modeling of air pollution in support of exposure and epidemiological assessments …

In the past few decades, extensive epidemiological studies have focused on exploring the
adverse effects of PM 2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) …

With increasing public concerns on air pollution in China, there is a demand for long-term
continuous PM 2.5 datasets. However, it was not until the end of 2012 that China …

Urban air pollution is one of the most critical issues that affect the environment, community
health, economy, and management of urban areas. From a public health perspective, PM …

Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM 2.5) profoundly
affects environmental systems and human health. To dynamically monitor fine particulate …