Exploring the combined association between road traffic noise and air quality using QGIS

Wisdom K. Adza*, Andrew S. Hursthouse, Jan Miller, Daniel Boakye

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
20 Downloads (Pure)


There is mounting evidence that exposure to air pollution and noise from transportation are linked to 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 in this project to monitor traffic noise on Glasgow's streets at specific places during peak hours. The Quantum GIS program's numerical modelling of road noise and air quality was used to analyse the combined association of noise and air pollution (abbreviated QGIS). Based on the geospatial, data on residential was added by replicating annual average air pollutant concentrations, including particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), and road-traffic noise at different components frequencies (Lden). The combined relationships between air pollution and traffic noise at different component frequencies was 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 map was influenced significantly by NO2, PM10 and PM2.5 and Lden at all monitoring locations but EDK. Studies that only consider one of these two related exposures run the danger of exaggerating the impact of the exposure under study while underestimating the combined impact of the two environmental exposures.
Original languageEnglish
Article number192417057
Number of pages19
JournalInternational Journal of Environmental Research and Public Health
Issue number24
Publication statusPublished - 19 Dec 2022


  • environmental noise
  • air quality
  • environmental pollution
  • transport
  • environmental public health
  • cardiovascular disease
  • Quantum Geographic Information System


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