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

The Getis-Ord Gi* 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 fr...

Full description

Saved in:
Bibliographic Details
Published in:Sustainability 2022-08, Vol.14 (17), p.10461
Main Authors: Vizuete-Jaramillo, Efrain, Meza-Figueroa, Diana, Reyes-Castro, Pablo A, Robles-Morua, Agustin
Format: Article
Language:English
Subjects:
Air pollution
Analysis
Arid regions ecology
Cities
Climate change
Clustering
Coronaviruses
Dust
Environmental aspects
Environmental health
Environmental studies
Geospatial data
Health risk assessment
Health risks
Outdoor air quality
Poisons
Public health
Sensitivity analysis
Spatial analysis
Spatial data
Spatial distribution
Traffic congestion
Urban areas
Urban ecology
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Getis-Ord Gi* 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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su141710461