A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach

Estimating pollutant concentrations at a local and regional scale is essential in environmental and health policy decision making. Here we present a novel land use regression (LUR) modelling methodology that exploits the high temporal resolution of fixed-site monitoring (FSM) to produce a national-scale air quality model for the key pollutant NO2. The methodology partitions concentration time series from a national FSM network into wind-dependent sectors or “wedges”. A LUR model is derived using predictor variables calculated within the directional wind sectors, and compared against the long-term average concentrations within each sector.