The study we present in this paper aims at characterizing the range of fine particulate matter (PM2.5) and black carbon (BC) concentrations to which bike-path users in Bogotá are exposed to. Using a bike equipped with a DustTrak and a micro-aethalometer we measured PM2.5 and BC concentration levels along bike-paths corridors, during weekdays and weekends. Experiments were conducted in fours streets, representing four typical configurations of bike-paths in the city. Traffic data for workdays was also available from local mobility authority. Results indicate that bike-paths users in Bogota are exposed to air pollution levels far exceeding the threshold values established as potentially dangerous for human health. Average concentrations for PM2.5 ranged between 80 and 136 ug/m3 in workdays and between 30 and 72 ug/m3 in weekends. BC mean concentrations were between 16 and 38 µg/m3 during workdays and in the range 10 to 32 µg/m3 during weekends. A statistically significant difference exists in the levels of pollutants concentrations measured during workdays and weekends for all the considered bike paths. According to our results, both traffic volume and diffusions conditions, which are affected by many factors including street geometry, affect bike-path user’s exposure levels. Taking into account the important role that bicycling is playing as an alternative transport mode in Latin American cities, we consider these results provide useful insights to increase the appreciation of the excessive bikers´ exposure to air pollution in Bogotá. Moreover, these findings contribute with technical elements that should lead to the inclusion of air quality variable when designing and planning sustainable urban mobility infrastructures.

Reducing greenhouse gas (GHG) emissions is one of the main challenges to stopping climate change; in particular, the building sector acts as one of the main emitters. In response, governments have been developing nationally determined contributions (NDCs) and roadmaps to establish measures to achieve net-zero emissions. One of the main barriers to implementing these measures, particularly in the building sector, underlays in stakeholder integration. Through social network analysis (SNA) concepts, this research explores the roles of the actors whose participation is required in decarbonizing the Colombian building sector. These stakeholders engage in enabling categories (e.g., policy, finance, technology, and capacity development) required to achieve net-zero emissions, according to the goals proposed by the Colombian Green Building Council (CGBC). For the network analysis, we employ the results of semi-structured interviews led by the CGBC within the framework of the Zero Carbon Building Accelerator (ZCBA) project. Findings show highly interconnected networks characterized by redundant connections among actors. Three types of actors are identified within each enabler network: prominent actors with high centrality values, in charge of coordinating most of the actions required to achieve net-zero emissions; second-level actors limited to specific roles in policy-making processes; and perimeter actors focused on a few particular actions.

Air quality in Bogotá, Colombia, has become of increasing concern. Especially, the levels of PM ₁₀ are alarming, because of their relation to health risks. A forecast system for PM ₁₀ levels is beneficial for developing preventive policies of environmental authorities. This paper proposes different forecasting models of particulate matter obtained with three machine learning techniques. A dataset from 8 air quality monitoring stations including PM ₁₀ and environmental measurements was constructed. Three selection methods of relevant variables for prediction were assessed: selecting variables with the assistance of an expert group, and using two automatic selection methods. Having three sets of potential variables to use as an input, three different forecasting methods were implemented: logistic regression, classification trees and random forest. Finally, a validation and comparison of results are made, to conclude about the best forecast model to be implemented for the city.

In this work we apply a rigorous and reproducible data analytics process for validation and analysis of the historical data from Bogotá (Colombia) air quality monitoring network since 1998. The reasons for addressing this research study stem from the lack of a consistent approach for cleaning, validating and reporting air quality data. By analyzing the whole dataset, we are aiming at providing citizens and the city authorities with a clear view of the current situation of air quality and of its historical evolution. Without any loss of generality, we focus our analysis on both respirable and fine particulate matter (PM₁₀ and PM₂.₅) concentrations, which in Bogotá and worldwide are source of concern for their negative impacts on human health. We develop a reproducible and flexible data cleaning methodology for particulate matter concentration data reported by the local authorities, which allows customizing and applying configurable validation rules. Then, we present statistical descriptive analyses by providing intuitive data visualizations, characterizing historical and spatial change of air pollutant levels. Results raise concerns for the high percentage of invalid data, as well as the high levels of PM₂.₅ and PM₁₀ ambient concentrations as observed in the valid portion of the available data, which frequently exceed national and international air quality standards. The data exhibit encouraging signs of air quality improvement, particularly for PM₁₀. However, the analyses indicate that significant differences exist across Bogotá, and particularly in the south-west zone of the city annual concentrations of particulate matter are up to three or four times the WHO recommendations. We are confident on the methodology and results from our analysis are useful both for local environmental authority and the general public to help in obtaining consistent conclusions from the available data.

Objetivo. Este estudio piloto tuvo como objetivo caracterizar la contaminación del aire a la que están expuestos los peatones en inmediaciones de tres vías con alto tráfico vehicular en la ciudad de Bogotá (carrera novena, carrera séptima y carrera once). Método. Como indicador de la calidad del aire se utilizó el carbono elemental (BC), dado que es un compuesto reconocido como trazador de las emisiones provenientes de fuentes móviles. Las concentraciones de BC fueron registradas de forma continua y en tiempo real con un MicroAethalometro portátil Modelo AE51 que era portado por las personas que realizaban los recorridos de 1.6 kilómetros en cada vía, al tiempo que diligenciaban un formato de actividades en el que consignaban los eventos relevantes percibidos durante el recorrido. Resultados. Re encontró una relación directa (observada) entre los picos de concentración de BC a los que están expuestos los peatones y los eventos documentados en cada trayecto. Si bien en los tres corredores viales estudiados se registraron concentraciones de BC que indican un aire de inadecuada calidad, se encontraron distintas distribuciones de las concentraciones para cada vía estudiada. Los autores atribuyen este resultado a la diferencia en el volumen de tráfico pesado (buses y busetas principalmente), siendo la Carrera Once la vía con mayor flujo de vehículos de este tipo y más altas concentraciones de BC. Conclusión. La evidencia recolectada refuerza la importancia de involucrar la variable calidad del aire al momento de diseñar infraestructura orientada a modos de transporte no motorizado.