Land use regression (LUR) models are widely used in epidemiological and environmental
studies to estimate humans' exposure to air pollution within urban areas. However, the early …

Air pollutant forecasting can be used to quantitatively estimate pollutant reduction trends.
Combining bibliometrics with the evolutionary tree and Markov chain methods can achieve a …

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 …

With rapid economic growth, urbanization and industrialization, fine particulate matter with
aerodynamic diameters≤ 2.5 µm (PM 2.5) has become a major pollutant and shows …

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 …

Air pollution is a major global environmental problem that affects health. In view of this, it is
important to improve the prediction method of air pollutant concentrations to obtain accurate …

It is likely that exposure surrogates from monitoring stations with various limitations are not
sufficient for epidemiological studies covering large areas. Moreover, the spatiotemporal …

It is generally established that PCDD/Fs is harmful to human health and therefore extensive
field research is necessary. This study is the first to use a novel geospatial-artificial …

With the rapid development of the Internet of things (IoTs) and modern industrial society,
forecasting air pollution concentration, eg, the concentration of PM 2.5, is of great …

Kriging interpolation and land use regression (LUR) have characterized the spatial
variability of long-term nitrogen dioxide (NO 2), but there has been little research on …