Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model

Featured Application Modeling the spatial distribution of PM2.5 with personal monitors and an LUR approach may be useful for local authorities to make accountable public policy decisions. Abstract In this study, the spatial distribution of PM2.5 air pollution in Mexico City from 37 personal exposures was modeled. Meteorological, demographic, geographic, and social data were also included. Geographic information systems (GIS), spatial analysis, and Land-Use Regression (LUR) were used to generate the final predictive model and the spatial distribution map which revealed two areas with very high concentrations (up to 109.3 mu g/m(3)) and two more with lower concentrations (between 72 to 86.5 mu g/m(3)) (p < 0.05). These results illustrate an overview trend of PM2.5 in relation to human activity during the studied periods in Mexico City and show a general approach to understanding the spatial variability of PM2.5.