Análisis de contaminantes atmosféricos de criterio ante escenarios de cambio climático en la Ciudad de Bogotá : un estudio desde la sustentabilidad

Abstract

Air quality is a topic that has become of primary relevance in cities and metropolitan areas due to the high risks to the health and well-being of the population (Hernández, 2017). Information from the World Health Organization (WHO) identified air pollution in the world causing approximately 3 million premature deaths per year, of which 88% occur in low- and middle-income countries. Air quality in cities is even more important when it is mentioned that it is highly dependent on climate and therefore sensitive to climate change. The Intergovernmental Panel on Climate Change (IPCC) has proposed four climate change scenarios identified as RCP 2.6, 4.5, 6.0, and 8.5. The IDEAM through the third communication on climate change welcomed these scenarios at the Colombian level, showing a temperature anomaly and modifications for each scenario, with RCP 2.6 having the least impact and RCP 8.5 being the most critical. The present study evaluated air quality through the historical records (2007-2018) of the Bogotá Air Quality Monitoring Network (RMCAB) and generated models that allowed forecasting air quality for each scenario of climate change. The results indicated that 44% of the stations of the RMCAB will present some non-compliance related to the air quality standard (Res. 2254 of 2017), non-compliances for PM10 are mainly presented in scenarios RCP 2.6, RCP 4.5 and RCP 6.0 in the stations that currently have important of this pollutant; Non-compliances due to ozone (O3) are presented at different stations in the RCP 8.5 scenario. Finally, a general review was carried out on the initiatives and strategies that are being implemented worldwide for the average improvement of air quality, identifying their visibility (PV), which include initiatives for green walls and roofs (PV: 170.5 and 119), forest planting (PV: 138.5), improvement and control over fuels (PV: 99), forecasting through models (PV: 25.5) and generation of comprehensive public policies for sustainable development ( PV: 52.5).