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

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Geospatial Analysis

Background:

• Accurate estimation of air pollutant concentrations is vital for environmental and health policy.
• Traditional land use regression (LUR) models often require extensive, purpose-designed monitoring campaigns.
• Nitrogen dioxide (NO2) is a key pollutant with significant local and regional impacts.

Purpose of the Study:

• To develop a novel LUR modelling methodology for national-scale air quality assessment of NO2.
• To leverage high temporal resolution fixed-site monitoring (FSM) data for improved LUR modelling.
• To assess the spatial variability of NO2 concentrations across the Republic of Ireland.

Main Methods:

• Partitioning FSM concentration time series into wind-dependent directional sectors ('wedges').
• Developing LUR models using predictor variables within these directional sectors.
• Comparing model performance against long-term average concentrations within each sector.

Main Results:

• The novel LUR model captured 78% of the spatial variability in NO2 across Ireland.
• The methodology achieved high accuracy using approximately half the monitoring points of traditional LUR models.
• Incorporating emission source-receptor position improved empirical LUR model structure.

Conclusions:

• The developed LUR methodology offers an efficient and effective approach for national air quality modelling.
• The model supports applications in environmental exposure, human health studies, and policy decision-making.
• The methodology has potential applicability for other pollutants and regions with adequate monitoring networks.