Introducción. La Organización Mundial de la Salud señala que tres millones de muertes al año por enfermedades cardiopulmonares están relacionadas con la exposición a la contaminación del aire.Objetivo. Estimar las superficies de concentración de partículas en suspensión de menos de 2,5 μm (Particulate Matter, PM2,5) en Bogotá entre el 2014 y el 2015, clasificándolas según las guías de calidad del aire de la Organización Mundial de la Salud para enfermedades cardiopulmonares.Materiales y métodos. Se hizo un estudio ecológico mediante técnicas geoestadísticas. Se calcularon los promedios de PM2,5 en lapsos de seis horas a lo largo del día en cuatro franjas horarias. Las concentraciones se clasificaron según los valores diarios y anuales de las guías de calidad del aire de la OMS.Resultados. La localidad de Kennedy presentó las mayores concentraciones de PM2,5 en todas las franjas horarias. Los valores registrados en esta zona y clasificados según las guías diarias y anuales de calidad del aire, evidenciaron que la localidad presentaría un incremento de 1,2 % en la mortalidad cardiopulmonar en el corto plazo y de 9 % en el largo plazo. Conclusión. Las franjas horarias de las 0:00 a las 6:00 h y de las 12:00 a las 18:00 h, cumplieron con el valor anual de las guías de calidad del aire de 10 μg/m3 en una parte de la zona oriental de la ciudad. En el resto de la ciudad, en las franjas horarias de las 6:00 h a las 12:00 h y de las 18:00 h a las 24:00 h se registraron valores que cumplían los objetivos intermedios 2 y 3, lo que representa incrementos de 9 y 3 % en la mortalidad cardiopulmonar, respectivamente.

Thousands of deaths associated with air pollution each year could be prevented by forecasting the behavior of factors that pose risks to people's health and their geographical distribution. Proximity to pollution sources, degree of urbanization, and population density are some of the factors whose spatial distribution enables the identification of possible influence on the presence of respiratory diseases (RD). Currently, Bogotá is among the cities with the poorest air quality in Latin America.Specifically, the locality of Kennedy is one of the zones in the city with the highest recorded concentration levels of local pollutants over the last 10 years. From 2009 -2016, there were 8619 deaths associated with respiratory and cardiovascular diseases in the locality. Given these characteristics, this study set out to identify and analyze the areas in which the primary socio-economic and environmental conditions contribute to the presence of symptoms associated with RD. To this end, information collected in field by performing georeferenced surveys was analyzed through geostatistical and machine learning tools which carried out cluster and pattern analyses. Random forests and AdaBoost were applied to establish hotspots where RD could occur, given the conjugation of predictor variables in the micro-territory. It was found that random forests outperformed AdaBoost with 0.63 AUC. In particular, this study's approach applies to densely populated municipalities with high levels of air pollution. In using these tools, Municipalities can anticipate environmental health situations and reduce the cost of respiratory disease treatments.

Spatio-temporal analysis of the hydrological response to land cover changes in the sub-basin of the Chicú river, Colombia.

Rapid urbanization contributes to the development of phenomena such as climate variability, especially in tropical countries, which negatively impact ecosystems and humans, factors that influence urban sustainability. Additionally, the increase of building construction prevents the flow of wind streams contributing to the retention of pollutants and hot air masses, causing events such as urban heat islands (UHI). This study aimed to analyze from the micro-territorial level, the influence of urban growth on the UHI phenomenon over the last two decades in the locality of Kennedy, in Bogotá, Colombia. For this purpose, environmental and socio-economic factors were evaluated. For the former, Landsat satellite images and spectral indices were used to evaluate the spatial-temporal variation in the quantity and quality of vegetation, bodies of water, urbanized areas, impervious surfaces, as well as to calculate the land surface temperature and its distribution in the study area. With regard to the socio-economic factors, the variables considered for analysis were population density and energy consumption. Lastly, a principal component analysis was carried out to identify possible associations between the variables and to identify the contribution of each micro-territory to the UHI phenomenon in the study area. The spatio-temporal variations reveal a growing trend over time, especially in impermeable areas where several economic activities, vehicular traffic, and population density converge, which require certain actions to be prioritized in territorial planning and the addition of public green spaces in urban zones.

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