Particulate matter (PM) is a widely used indicator of air quality. Satellite-derived aerosol
products such as aerosol optical depth (AOD) have been a useful source of data for ground …

Increasing availability of data related to air quality from ground monitoring stations has
provided the chance for data mining researchers to propose sophisticated models for …

Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate
hydrological forecasting. To improve the spatial resolution and quality of satellite …

Several studies have attempted to predict ground PM 2.5 concentrations using satellite
aerosol optical depth (AOD) retrieval. However, over 70%–90% of aerosol retrievals are non …

Fine particulate matter such as PM 2.5 has been the focus of increasing public concerns
because of its adverse effect on environment and health risks. However, existing efforts of …

Geographically weighted regression (GWR) and geographically and temporally weighted
regression (GTWR) are classic methods for estimating non-stationary relationships …

Air pollution assessment in urban and rural areas is really challenging due to high spatio-
temporal variability of aerosols and pollutants and the uncertainties in measurements and …

Haze pollution is an increasingly serious problem in China. Based on the PM 2.5 data from
283 prefecture-level cities in China, we combine the stochastic impacts by regression on …

Satellite-derived aerosol optical depth (AOD) has been widely used to estimate ground-level
PM 2.5 concentrations due to its spatially continuous observation. However, the coarse …

While numerous studies have explored the spatial patterns and underlying causes of PM 2.5
at the urban scale, little attention has been paid to the spatial heterogeneity affecting PM 2.5 …