Exploring the Combined Association between Road Traffic Noise and Air Quality Using QGIS

There is mounting evidence that exposure to air pollution and noise from transportation are linked to the risk of hypertension. Most studies have only looked at relationships between single exposures. To examine links between combined exposure to road traffic, air pollution, and road noise. A Casella CEL-63x instrument was used to monitor traffic noise on a number of locations in residential streets in Glasgow, UK during peak traffic hours. The spatial numerical modelling capability of Quantum GIS (abbreviated QGIS) was used to analyse the combined association of noise and air pollution. Based on geospatial mapping, data on residential environmental exposure was added using annual average air pollutant concentrations from local air quality monitoring network, including particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), and road-traffic noise measurements at different component frequencies (Lden). The combined relationships between air pollution and traffic noise at different component frequencies were examined. Based on Moran I autocorrelation, geographically close values of a variable on a map typically have comparable values when there is a positive spatial autocorrelation. This means clustering on the map was influenced significantly by NO2, PM10 and PM2.5, and Lden at the majority of monitoring locations. Studies that only consider one of these two related exposures may exaggerate the impact of the individual exposure while underestimating the combined impact of the two environmental exposures.

Automated summary

There is increasing evidence linking exposure to air pollution and noise from transportation to the risk of hypertension. Most studies have only focused on individual exposures. This study used GIS technology to analyze the combined impact of traffic noise and air pollution. The findings showed that clustering on the map was significantly influenced by air pollution and traffic noise at most monitoring locations. The study suggests that considering both of these exposures is important to accurately assess their combined impact.