Proximate pollutant data can provide information for land-use predictors in LUR models,
when coupled with spatial interpolation of ambient pollutant measurements, may provide …

Accurate measurements of ground-level PM 2.5 (particulate matter with aerodynamic
diameters equal to or less than 2.5 μm) concentrations are critically important to human and …

Abstract Fine particulate matter (PM 2.5) is the major air pollutant in Beijing, posing serious
threats to human health. Land use regression (LUR) has been widely used in predicting …

Abstract Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK)
interpolation have been widely used to offset the shortcomings of PM2. 5 data observed at …

Ambient fine particulate matter (PM 2.5) has been ranked as the sixth leading risk factor
globally for death and disability. Modelling methods based on having access to a limited …

Ambient fine particulate matter (PM 2.5) plays an important role in cardiovascular-and
respiratory-related death. Empirical statistical models have been widely applied to estimate …

Meteorology, human activities, and other emission sources drive diurnal cyclic patterns of air
pollution. Previous studies mainly focused on the variation of PM 2.5 concentrations during …

The intraurban distribution of PM 2.5 concentration is influenced by various spatial,
socioeconomic, and meteorological parameters. This study investigated the influence of 37 …

High resolution pollution maps are critical to understand the exposure and health effect of
local residents to air pollution. Currently, none of the single technologies used to measure or …

Most time-sequenced ambient air pollution data in China is published through daily Air
Quality Index (AQI). However, few studies have used the AQI data to calibrate satellite-based …