![]() ![]() Low concentrations of non-essential heavy metals (As, Hg, Pb, Cr, and Cd) can be lethal to animals. The severity of health problems due to heavy metal toxicity depends on several factors, such as the type and form of the element, the route and duration of exposure, and to a greater extent, the susceptibility of each person. Due to their abundance, toxicity, persistence, and bioaccumulation, heavy metals can cause permanent damage to ecosystems and humans. Heavy metals are some of the major pollutants found in road dust. In terms of country-level data, researchers have tested the environmental Kuznets curve (EKC) hypothesis, which states that the relationship between gross domestic product (GDP) per capita and different environmental indicators exhibit an inverted-U curve. From a social point of view, aspects such as tax revenue and education level are associated with a decrease in urban pollution. Studies found that urban structure factors, such as land use, industrial development, and building construction, worsened the pollution in urban areas. At the same time, road dust is a source of pollutants of atmospheric particulate matter. Research reported that road dust is a sink for polluting emissions, which are deposited on the surface of streets, sidewalks, and windows. Less attention has been paid to road dust pollution however, we assume that they are likely interlinked. Manufacturing units were associated with an increase in Cu (significance level of 95%), while the entropy index was associated with an increase in Ni (significance level of 95%).Īir pollution has attracted a great deal of attention, as it is considered one of the main causes of death in cities therefore, air pollution has been widely researched. The distance to the airport had a weak (significance level of 90%) and inverse relationship with Pb. The median strip area in the roads had a weak (significance level of 90%) but consistent positive relationship with Cr, Cu, Ni, Pb, and the PLI. ![]() Most variables failed to detect any relationship with heavy metals. The results indicated low levels of positive spatial autocorrelation for all heavy metals. We did this by using a spatial autocorrelation indicator (Global Moran’s I) and applying ordinary least squares (OLS) and spatial regression models. Firstly, to analyze the spatial correlation of heavy metals, and secondly, to identify the main factors that influenced the heavy metal concentrations in the road dust of Mexico City. After estimating the mostly anthropogenic origin of these pollutants based on global levels of reference, there were two main aims of this study. We collected 482 samples of road dust, we determined the concentrations of five heavy metals (Cr, Cu, Pb, Zn, and Ni) using inductively coupled plasma optical emission spectrometry (ICP-OES), and then we derived the pollution load index (PLI). Pollutants, such as heavy metals, have been found in urban road dust however, it is unclear whether the urban form has a role in its accumulation, mainly in cases where there is no dominant unique source. ![]() Environmental pollution is a negative externality of urbanization and is of great concern due to the fact that it poses serious problems to human health. ![]()
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