Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China
Fine particulate matter (PM₂.₅) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM₂.₅by building separate LUR models in Beijing. Hourly routine PM₂.₅measurements were collected at 35 sites from 4th March 2013 to 5th March 2014.