Geospatial modeling of heavy metals concentrations in urban road dust

Details

Supervisors

Patrick bogaert ; Verbanck, Michel

Faculty

Faculté des bioingénieurs

Degree label

Master [120] : bioingénieur en sciences agronomiques, à finalité spécialisée

Abstract

The aim of this master thesis is to perform an in-depth analysis of the spatial distribution of heavy metals (HMs) concentrations in road dust (RD) in a municipality of the Brussels Capital-Region (BCR) and identify the most relevant proxies (i.e., proxy variables) for geospatial modeling. For this goal, 128 samples of RD were collected in the Municipality of Anderlecht (AM) over 3 consecutive years (2019-2021). The concentrations of Cd, Cr, Cu, Ni, Pb and Zn in the finest fraction of these samples (ø < 250 𝜇𝑚) were determined by ICP-OES. Based on a literature review, several continuous and categorical proxies were collected to build a geospatial dataset at the scale of the BCR. For each heavy metal, the best candidate Multivariate Linear Regression (MLR) model was selected by a forward stepwise procedure. Their ability to predict HMs concentrations remains limited with 𝑅²𝑎 values around 0.5 for all models. The most relevant proxies were : "Distance from the center of the BCR", "Land use", "Road hierarchy" and "Roadside parking occupation". Investigation of the models residuals trough a spatial dependence analysis revealed the potential omission of one or multiple proxies in our analyses. These MLR models were used afterwards to predict HMs concentrations for all the road segments of the AM. Despite the moderate performances of the models, we believe that the resulting maps provide useful information for decision-making regarding mitigation measures for the local authorities and public services. We also believe that our predictions could be extended to the whole BCR and that our approach is reproducible for other urban areas. The results presented in this master thesis were summarized and disseminated in a publication.