In 2023, Murcia implemented a new sustainable urban Mobility Plan emphasizing pedestrianization, segregated public transportation, and the reduction of private vehicle traffic within the city. Consequently, this study aimed to evaluate heavy metal pollution in urban dust by comparing data collected prior to the plan’s implementation (2016) with that obtained afterward (2025). Systematic sampling of urban dust was conducted at 84 sites during both study periods. The concentrations of elements were quantified employing inductively coupled plasma mass spectrometry. The K-means algorithm was employed to identify data clusters. The analysis revealed a distinct geochemical shift, with the dominant elemental abundance changing from Fe > Zn > Mn > Pb > Cu > Cr in 2016 to Fe > Zn > Mn > Cu > Cr > Pb by 2025. Concentrations of Pb and Mn declined following the implementation of the Mobility Plan, whereas levels of As exhibited an increase between 2016 and 2025. Children are at risk of exposure arising from As, which was identified as the contaminant of greatest concern. The Murcia Mobility Plan, implemented in 2023, had a significant and multifaceted impact. It led to a decrease in traditional metal contaminants and simultaneously highlighted the emergence of new contaminants, such as arsenic (As).
M. J. Delgado-Iniesta, Pura Marín-Sanleandro, Ángeles Gallegos et al.· Urban Science· 0 citations
Heavy metal contamination in urban street dust is often highly heterogeneous, limiting the effectiveness of conventional geostatistical mapping approaches. In Mexico City, previous studies have reported very low spatial autocorrelation for key elements, making interpolation-based methods unsuitable for representing contamination patterns. This study proposes a multiscale cartographic framework to analyze and visualize heavy metal contamination in street dust from 482 sampling sites using the contamination factor (CF) and pollution load index (PLI) at three levels of spatial analysis: (i) city-scale patterns identified through hierarchical clustering of districts based on median CF values, (ii) district-scale variability assessed through statistical comparisons of PLI distributions, and (iii) site-scale identification of contamination hotspots using observed PLI values. Results revealed five contamination clusters and significant differences in pollution load among districts (Kruskal–Wallis, p < 0.05); PLI values in Xochimilco and Tláhuac are significantly lower than in Cuauhtémoc, Gustavo A. Madero, and Magdalena Contreras. Higher contamination levels were concentrated in northern and central districts, whereas lower levels predominated in the south. Site-scale analysis identified localized hotspots associated with transportation infrastructure, industrial areas, and commercial corridors, reflecting the influence of local emission sources. The results demonstrate that contamination patterns operate simultaneously at city, district, and site scales and cannot be adequately represented through interpolation alone. The proposed framework provides a practical approach for visualizing heterogeneous contamination datasets, supporting environmental decision-making, and may apply to other metropolitan regions characterized by weak spatial autocorrelation and localized pollution processes.
A. Aguilera, Ángeles Gallegos, R. Cejudo et al.· Land· 0 citations
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