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

• Published in: The Science of the total environment Vol. 630; pp. 1324 - 1334
• Main Authors: Naughton, O., Donnelly, A., Nolan, P., Pilla, F., Misstear, B.D., Broderick, B.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.07.2018
• Subjects: Air pollution; GIS; Land use regression; Population exposure; Wind direction; GIS; Population exposure; Land use regression; Wind direction; Air pollution
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2018.02.317

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. •A novel LUR model using wind fields and fixed site monitoring is proposed.•8 wind-dependent sectors used to calculate predictor variables and partition NO2 concentrations•A model using 15 FSM sites captured 78% of the spatial variability in NO2.•Demonstrates ambient NO2 levels strongly influenced by traffic and wind direction.•Model applied at high resolution to develop a national air quality map [Display omitted]