LAUSR

Surface ozone is problematic to air pollution. It influences respiratory health. The air quality monitoring stations measure pollutants as surface ozone, but they are sometimes insufficient or do not have an adequate distribution for understanding the spatial distribution of pollutants in an urban area. In recent years, some projects have found a connection between remote sensing, air quality and health data. In this study, we apply an empirical land use regression (LUR) model to retrieve surface ozone in Quito. The model considers remote sensing data, air pollution measurements and meteorological variables. The objective is to use all available Landsat 8 images from 2014 and the air quality monitoring station data during the same dates of image acquisition. Nineteen input variables were considered, selecting by a stepwise regression and modelling with a partial least square (PLS) regression to avoid multicollinearity. The final surface ozone model includes ten independent variables and presents a coefficient of determination (R2) of 0.768. The model proposed help to understand the spatial concentration of surface ozone in Quito with a better spatial resolution.