Is the Urban Form a Driver of Heavy Metal Pollution in Road Dust? Evidence from Mexico City

Abstract

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. 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. 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). After estimating the mostly anthropogenic origin of these pollutants based on global levels of reference, there were two main aims of this study. 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. We did this by using a spatial autocorrelation indicator (Global Moran’s I) and applying ordinary least squares (OLS) and spatial regression models. The results indicated low levels of positive spatial autocorrelation for all heavy metals. Most variables failed to detect any relationship with heavy metals. 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. The distance to the airport had a weak (significance level of 90%) and inverse relationship with Pb. 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%).

Chemical Source Profiles and Toxicity Assessment of Urban Fugitive Dust PM2.5 in Guanzhong Plain, China

“…Using these influencing variables to calculate the linear regression equation, the form is as follows: where y is the dependent variable which represents the health risk of the heavy metal in urban dust; b 0 , b 1 …, b n are constants; x 1 , x 2 …, x n are the influence (independent) variables which were significantly related to dependent variables. To ensure the low risk of multicollinearity that could bias the coefficient, the variance inflation factor (VIF) of each influencing variable must be below 5 [ 30 ].…”

“…It has the advantages of low data requirements, complete consideration factors, high simulation accuracy, high spatial resolution, and a wide application range [ 27 ]. The LUR model was used to predict the pollutants in atmospheric PM 2.5 [ 28 , 29 ]; subsequently, the LUR method was increasingly used in explaining and predicting the heavy metals in urban dust or soil [ 26 , 27 , 30 , 31 ]. Thus, it is crucial to determine what characteristics of the urban form act as driving factors of heavy metal pollution in urban dust to obtain a better measurement that limits the exposure to heavy metals [ 30 ].…”

Assessment of heavy metal concentration and their relationship in beef sold in markets

“…Nonetheless, meat from the former market also had more arsenic than meat from the latter. Other findings (Akoto et al, 2014) proposed that an arsenic content of 0.01-0.06 mg/100g, though minute, can be detrimental to human health (Aguilera et al, 2021). This means that beef from the various markets would have more detrimental effects on consumers, but such effects would be more severe in Ashaiman than in Madina and Makola.…”

“…However, the value of meat as food for humans is affected by its relationship with cholesterol, saturated fat, hazardous contaminants, and persistent pollutants such as heavy metals (Püssa, 2013). Heavy metals are, and because they are not easy to transfer and linger over time, they persist in nature as toxic pollutants causing serious harm to living things (Aguilera et al, 2021). According to Fan et al (2021), metals that have a density greater than 4.5 grams per cubic centimeter are considered heavy metals.…”