A Decade of Air Quality in Bogotá: A Descriptive Analysis

Abstract

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 (PM 10 and PM 2 . 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 PM 2 . 5 and PM 10 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 PM 10 . 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.

A Decomposition-Ensemble Approach with Denoising Strategy for PM2.5 Concentration Forecasting

“…Hence, future research on PM 2.5 time series forecasting should highlight the following aspects, that is, the usage of other possible factors (meteorological factors in particular) as well as the application of the multiobjective optimization algorithm. In addition, it can also be practicable to establish deep learning-enabled approaches, which are more effective and have the potential to be another significant research direction for future work [12,47].…”

“…At present, many scholars have made a lot of research studies in this field. Accordingly, these studies are roughly divided into four categories: time series model [11][12][13][14], econometric model [15][16][17], artificial intelligence (AI) model [18][19][20], and the hybrid model [21,22]. In particular, the time series approach, as the traditional forecasting approach, is often used to predict PM 2.5 concentration.…”

Toward Cleaner Transport Alternatives: Reduction in Exposure to Air Pollutants in a Mass Public Transport

“…Yet, PM 2.5 and PM 10 frequently exceed the WHO and national air quality standards. 49 The mass-transit bus-based Transmilenio BRT system began operation in December of 2000. It was a cost-effective solution to a transportation crisis of a large and densely populated city without a metro system, and remains as the only mass transit system in the city.…”

“…Annual mean concentration of this pollutant has been decreasing in recent years. Yet, PM 2.5 and PM 10 frequently exceed the WHO and national air quality standards …”

Building an Equitable Society in Colombia

“…The latter has been estimated to cost the Colombian economy COP 15.4 trillion a year in Colombian cities alone, 12 or about 2 percent of GDP (DNP and Fondo Acción 2015). Beyond these general economic effects, reducing air pollution boosts equality, as air pollution tends to disproportionally affect the poorer strata of society (see, for example, Mura et al 2020). Second, by discouraging the overconsumption of private road transport, carbon pricing reduces road accidents which, again, disproportionately affect poorer segments of society (Nantulya and Reich 2002) and congestion, both of which represent a drag on growth and welfare (Pigato 2019).…”