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 …

Rapid urbanization and industrial growth have caused massive increase in the number and
the production capacities of oil industries. Such industries release a wide-range of ambient …

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

We developed Europe-wide models of long-term exposure to eight elements (copper, iron,
potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter< 2.5 …

During the last many years, the air quality of the capital city of India, Delhi had been
hazardous. A large number of people have been diagnosed with Asthma and other …

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 …

National-scale empirical models of air pollution (eg, Land Use Regression) rely on predictor
variables (eg, population density, land cover) at different geographic scales. These models …

Background: Chronic exposure to air pollution may prime the immune system to be reactive,
increasing inflammatory responses to immune stimulation and providing a pathway to …

This article provides an in-depth review of Gaussian atmospheric dispersion models, which
are mathematical tools used to predict the dispersion of pollutants in the atmosphere. Such …

Present research aimed to develop monthly and annual land-use regression (LUR) model
for simulation of particulate matter (PM) concentration using conventional linear regression …