Presença de metais pesados tóxicos no Sedimento Depositado em Rodovia (SDR) em diversos países: uma meta-análise

Abstract

Road-deposited sediments (RDS) are composed of a complex set of elements accumulated on the road surface which have the capacity to adsorb a wide variety of contaminants from various sources, especially traffic. Their contact with the human being poses serious risks to human health, therefore studying RDS contamination is important to develop pollution control strategies. Thus, the aim of this study was to develop a relationship involving the toxic heavy metals concentration in RDS (Cd, Pb, Hg, Cr, As, Cu, Zn) and socioeconomic factors (population, population density, land use, HDI, energetic matrix and number of vehicles). To accomplish that, it was used published data obtained from a number of experimental studies worldwide, along with local social and economic information. Therefore, a meta-analysis was performed using principal component analysis (PCA) and Multivariate Regression. PCA results showed an increasing contamination tendency with the number of inhabitants and commercial and industrial uses. In this way, highly urbanized cities (London, New York and Madrid) showed high levels of contamination. Although, the study showed some outliers (anomalous contamination feature for Shenzhen, Halifax, Huludao and Singapore), possibly linked to local characteristics. Multiple linear correlations showed different adjustments levels (R²=0,30 for each individual factor; R²=0,5396 for the group of all factors analyzes simultaneously), which indicated that the adjustment level is highly influenced by the integrative relationship of the local factors. The best adjustment level was achieved for the multiple linear regression, R²=0,743, after an outlier selection. Results allowed to adjust the pollution Road Deposited Sediment Pollution Index, This index may be useful to indicate the contamination level by heavy metal in RDS based on the aforementioned socioeconomic factors. The best adjustments were obtained by applying a second order regression, R²=0.9376. The may be useful for the development of cleaning strategies aimed at pollution control.