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Using a Sensitivity Analysis and Spatial Clustering to Determine Vulnerability to Potentially Toxic Elements in a Semiarid City in Northwest Mexico


  • Efrain Vizuete-Jaramillo

    (Departamento de Ciencias del Agua y del Medio Ambiente, Instituto Tecnológico de Sonora, Obregón 85000, Mexico)

  • Diana Meza-Figueroa

    (Departamento de Geología, Universidad de Sonora, Hermosillo 83000, Mexico)

  • Pablo A. Reyes-Castro

    (Centro de Estudios en Salud y Sociedad, El Colegio de Sonora, Hermosillo 83000, Mexico)

  • Agustin Robles-Morua

    (Departamento de Ciencias del Agua y del Medio Ambiente, Instituto Tecnológico de Sonora, Obregón 85000, Mexico
    Laboratorio Nacional de Geoquímica y Mineralogía (LANGEM), Instituto de Geología, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico)


The Getis-Ord G i * statistic clustering technique was used to create a hot spot exposure map using 14 potentially toxic elements (PTEs) found in urban dust samples in a semiarid city in northwest Mexico. The dust distribution and deposition in this city are influenced by the seasonal wind and rain from the North American Monsoon. The spatial clustering patterns of hot spots were used in combination with a sensitivity analysis to determine which variables most influenced the PTE hot spot exposure base map. The hot spots areas (%) were used as indicators of environmental vulnerability, and a final integrated map was selected to represent the highest vulnerability of PTEs with a 99% level of confidence. The results of the sensitivity analysis indicated that the flood zones and pervious and impervious zones were the most sensitive variables due to their weight in the spatial distribution. The hot spot areas were reduced by 60.4% by not considering these variables. The hot spot analysis resulted in an effective tool that allowed the combination of different spatial layers with specific characteristics to determine areas that present greater vulnerability to the distribution of PTEs, with impacts on public and environmental health.

Suggested Citation

  • Efrain Vizuete-Jaramillo & Diana Meza-Figueroa & Pablo A. Reyes-Castro & Agustin Robles-Morua, 2022. "Using a Sensitivity Analysis and Spatial Clustering to Determine Vulnerability to Potentially Toxic Elements in a Semiarid City in Northwest Mexico," Sustainability, MDPI, vol. 14(17), pages 1-25, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10461-:d:895128

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    References listed on IDEAS

    1. Sung-Min Kim & Yosoon Choi, 2017. "Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data," IJERPH, MDPI, vol. 14(6), pages 1-16, June.
    2. Arvind Sharma & R. K. Gupta & Akhilesh Tiwari, 2016. "Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, August.
    3. Diana Movilla-Quesada & Julio Rojas-Mora & Aitor C. Raposeiras, 2022. "Statistical Study Based on the Kriging Method and Geographic Mapping in Rigid Pavement Defects in Southern Chile," Sustainability, MDPI, vol. 14(1), pages 1-14, January.
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