Spatial distribution of magnetic material in urban road dust classified by land use and type of road in San Luis ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2020-07-04

AUTHORS

Anahi Aguilera, Juan Julio Morales, Avto Goguitchaichvili, Felipe García-Oliva, Cynthia Armendariz-Arnez, Patricia Quintana, Francisco Bautista

ABSTRACT

Industrial and vehicular emissions of particles cause multiple damages to human health due to concentration, size, and composition. These emissions contain magnetic particles; therefore, low-cost properties allow tracking and monitoring them. This study is aimed at identifying the primary sources of magnetic material in 100 samples of urban road dust from San Luis Potosí, Mexico, analyzing the influence of land use and the type of road on these particles. Magnetic susceptibility (χlf) and isothermal remanent magnetization at 0.7 T (IRM0.7T) were determined, as well as the iron and manganese content using X-ray fluorescence. The distribution of particles was examined by land use and type of road through geostatistical maps and variance analysis. The results showed that the iron and manganese content, χlf, and IRM0.7T were positively correlated, indicating a possible common origin. The primary sources identified were the iron smelter and laminator in the industrial park. Urban land use influenced the content of iron, manganese, and magnetic material in urban road dust. The land uses with the more significant transformation (industrial and mixed) presented the highest values of iron, manganese, χlf, and IRM0.7T. On the other hand, vehicular traffic was indirectly assessed through the type of road, influencing the magnetic signal of urban road dust and finding the highest signals in primary and secondary roads. Then, the magnetic properties allowed the tracking and monitoring of magnetic particles from industrial and vehicle emissions. More... »

PAGES

951-963

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11869-020-00851-5

DOI

http://dx.doi.org/10.1007/s11869-020-00851-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1128992928


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