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The aim of this study is: to better identify quartz veins and pegmatitic dykes in the granitoids by mapping; to delineate areas of coastal placers with reddish to black shading by heavy mineral enhancement; and finally to integrate all data into a Geographic Information System to improve our knowledge of the land-sea interface.
An aerial image from the National Aerial Orthophotography Plan (PNOA, 15 cm spatial resolution, from 2020) was used, and an ad hoc UAV (unmanned aerial vehicle) survey was carried out to capture very high spatial resolution images (< 3 cm) in 2024.
The results show that UAV is better than PNOA at identifying detailed structural lineaments and is the only survey to delineate heavy mineralised areas (2.60 to 88.20 m2) and to detect dykes and veins. During fieldwork, placer deposits were also observed buried up to 60 cm below the beach surface, forming sub-parallel horizontal layers. The reddish colour, mainly due to the abundance of garnets, is probably due to the metamorphic rocks near Vigo (gneiss, 2-mica schists and mica schists). Quartz veins and pegmatites could be an alternative source of critical minerals to these placers (e.g., cassiterite and spodumene).
In order to establish a spatial health respiratory risk model for Quito, Ecuador, an empirical model was computed considering data between 2013 and 2017, using the median data values in each parish of the city. The variables are: i) 46 Landsat-8 satellite images with less than 10% of cloud cover and some indexes (normalized difference vegetation index NDVI, Soil-adjusted Vegetation Index SAVI, etc.); ii) air quality data (nitrogen dioxide - NO2, Ozone - O3, particulate matter less than 2.5μ - PM2.5 and sulfur dioxide - SO2) obtained from local air quality network stations and; iii) the hospital discharge rates from chronic respiratory diseases (CRD). In order to establish a probability model to get a CRD, a logistic regression was used. The empirical model is expressed as the probability of occurrence during the studied time. All the procedures were implemented in R Studio. The methodology proposed in this work can be used by health and governmental entities to access the risk of getting a respiratory disease, considering an application of remote sensing in the environmental and health management programs.
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