Mr. Diogo Ayres-Sampaio Profile

Diogo Ayres-Sampaio

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Proceedings Article | 23 October 2012 Paper

The use of remotely sensed environmental data in the study of asthma disease

D. Ayres-Sampaio, A. Teodoro, A. Freitas, N. Sillero

Proceedings Volume 8531, 853124 (2012) https://doi.org/10.1117/12.974539

KEYWORDS: Tantalum, Data modeling, NOx, Remote sensing, MODIS, Roads, Atmospheric modeling, Satellites, Humidity, Earth observing sensors

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Despite the growing use of Remote Sensing (RS) data in epidemiological studies, several diseases, including asthma, have not been studied yet using RS potentialities. Asthma is a chronic inflammatory disorder of the airway that affects people of all ages throughout the world. The expression of this disease can be influenced by some environmental factors such as allergens, air pollution or climate conditions. In this study, we modeled the distribution of asthma in each season, using Maximum entropy (Maxent) model and presence data obtained from a national database with asthma public hospitals admissions in Mainland Portugal, with discharges between years 2003 and 2008. We considered data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to retrieve estimates of near-surface air temperature and relative humidity. Land-use regression (LUR) models were developed to produce estimates of three pollutants: PM₁₀, NO₂, and CO. Moreover, MODIS Normalized Difference Vegetation Index (NDVI) was also used in the construction of Maxent models. All Maxent models predicted similar suitable areas and obtained acceptable area under the curve (AUC) values (~0.75) of the ROC plot. Our results show a strong relationship between asthma presence and NO₂, suggesting that asthmatic people living in urban areas with high traffic volume have an increased risk of suffering asthma attacks. Furthermore, there is evidence of the effect of PM₁₀, CO, and RH (during the Summer) in asthma expression. RS data have a great potential but also presents limitations that should be addressed to allow studying more complex diseases.

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