Using Inverse Distance Weighting to Determine Spatial Distributions of Airborne Chemical Elements

Air pollution due to heavy metals has become a major problem in urban centers worldwide. Tree barks provide measurements of particulate concentration with an indication of the associated chemical composition. Assessing the air quality in a region is of paramount importance in ensuring the proper concentrations of particles in the environment, and their spatial location and distribution. Spatial modelling is a fundamental element in the tool chain for managing ambient air quality in a region. This study uses inverse distance weighting in the analysis of chemical elements from the barks of 254 trees to map heavy metals in the air of the city of Douala, Cameroon during the dry seasons (December to February, when monthly rainfall is low) of 2022. The ANCOVA model was used to compare metal concentrations in the bark with the dendrometric parameters of trees. The results may help in monitoring the spread of heavy metals in the city of Douala, in order to pinpoint the sites at risk. Our findings show that the whole city is in a state of emergency with respect to air quality, and mitigation measures must be taken rapidly throughout its territory, especially in intersections with high traffic volumes and industrial zones. We suggest planting several tall trees with large crown volumes and thick bark to reduce air pollution. Phenology and foliage appearance in trees can provide information on the type of metal-related pollution. These findings bring additional evidence that trees can act as bio-indicators and bio-accumulators in urban environments.