Over the past few decades, the relationship between air pollution and urban forestry has been receiving increasing consideration as global cities have undergone rapid transformation. Urbanisation has resulted in population densification and increased air pollution due to the increased anthropogenic sources. Consequently, urban forestry has been proposed as one of the solutions as it has the potential to mitigate and ameliorate urban air pollution. This research investigated the spatial extent of four air pollutant concentrations and urban forestry to determine the relationship between air pollution concentrations and urban forestry across Sydney, Australia.
Ambient air pollutant concentrations and other variables such as land cover, population density, dwelling density, were combined to create a Land Use Regression (LUR) model to develop predictive models for urban CO, NO₂, SO₂, and PM₁₀ concentrations. Differences in pollutant concentrations were assessed with ArcGIS and analysis of covariance across various land cover types; active vegetation, non-active vegetation and bare ground. The relative influence of predictor variables for pollutant concentrations were determined using a stepwise multiple linear regression.
An inverse relationship between urban forestry and air pollution was observed and quantified in the land cover model. Furthermore, tree canopy cover was negatively correlated with all four air pollutants and urban indicators of pollution including dwelling density, population density and traffic count was positively correlated with the pollutants.
This LUR model established a statistically significant spatial relationship between urban forestry and air pollution mitigation and amelioration. These findings confirm urban forestry’s capabilities to mitigate and ameliorate air pollution on a city-wide scale. Furthermore, these findings could be incorporated in to or used to develop urban planning and greening policies whilst promoting urban forestry uptake in Sydney
