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. Validation results, based on 15 FSM training sites, show that the model captured 78% of the spatial variability in NO2 across the Republic of Ireland. This compares favourably to traditional LUR models based on purpose-designed monitoring campaigns despite using approximately half the number of monitoring points. Results also demonstrate the value of incorporating the relative position of emission source and receptor into the empirical LUR model structure. We applied the model at a high-resolution across the Republic of Ireland to enable applications such as the study of environmental exposure and human health, assessing representativeness of air quality monitoring networks and informing environmental management and policy makers. While the study focuses on Ireland, the methodology also has potential applicability for other criteria pollutants where appropriate FSM and meteorological networks exist.