Indoor 222-Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Abstract

Radon (222-Rn) is a naturally occurring gas that represents a health threat due to its causal relationship with lung cancer. Despite its potential health impacts, several regions have not conducted studies, mainly due to data scarcity and/or economic constraints. This study aims to bridge the baseline information gap by building an interactive dashboard that uses inferential statistical methods to estimate indoor radon concentration’s (IRC) spatial distribution for a target area. We demonstrate the functionality of the dashboard by modeling IRC in the city of Bogotá, Colombia, using 30 in situ measurements. IRC measured were the highest reported in the country, with a geometric mean of 91 ±14 Bq/m3 and a maximum concentration of 407 Bq/m3. In 57 % of the residences RC exceeded the WHO’s recommendation of 100 Bq/m3. A prediction map for houses registered in Bogotá’s cadaster was built in the dashboard by using a log-linear regression model fitted with the in situ measurements, together with meteorological, geologic and building specific variables. The model showed a cross-validation Root Mean Squared Error of 56.5 Bq/m3. Furthermore, the model showed that the age of the house presented a statistically significant positive association with RC. According to the model, IRC measured in houses built before 1980 present a statistically significant increase of 72 % compared to those built after 1980 (p-value = 0.045). The prediction map exhibited higher IRC in older buildings most likely related to cracks in the structure that could enhance gas migration in older houses. This study highlights the importance of expanding 222-Rn studies in countries with a lack of baseline values and provides a cost-effective alternative that could help deal with the scarcity of IRC data and get a better understanding of place-specific variables that affect IRC spatial distribution.

Martín Domínguez Durán

MSc. Geo-Information Science Student

My research interests include environmental health, data science, deep learning and remote sensing.