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Statistics, Environmental sciences, Meteorology and atmospheric sciences
Estimating pollutant concentrations at a local and regional scale is essential for good ambient air quality information in environmental and health policy decision making. Here we present a land use regression (LUR) modelling methodology that exploits the high temporal resolution of fixed-site monitoring (FSM) to produce viable air quality maps. 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. This study demonstrates the value of incorporating the relative position of emission source and receptor into the empirical LUR model structure. In our specific application, a model based on 15 FSM training sites 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 in model development. We applied the LUR equation 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.
Naughton, O. et al. (2018) A Land Use Regression Model for Explaining Spatial Variation in Air Pollution Levels using a Wind Sector Based Approach, Science of The Total Environment,Vol. 630, 15 July 2018, 1324–1334pp. doi.org/10.1016/j.scitotenv.2018.02.317