Surface, Satellite Ozone Changes in Northern South America During Low Anthropogenic Emission Conditions: A Machine Learning Approach

Rev.Fac.Ing.Univ.Antioquia

Córdoba²,

Sánchez-Cárdenas³

et al. 2022

Self Cite

Already published studies found that 20% of the atmospheric pollution variations are linked to local meteorology. This relationship may be more important in places with dense populations such as Latin American cities since the topography and fast multiscale changes are part of the tropical climate. Even so, this possibility has not been addressed in previous studies. This research aims to characterize the relationship between tropical climate variables and PM2.5 levels during high pollution events. The relationship between wind field, Turbulent Kinetic Energy (TKE), radiation, temperature, relative humidity, boundary layer height, and atmospheric stability with PM2.5 concentration was investigated. Statistical correlations and the parcel method were used to analyze the relationship between vertical motions and PM2.5. Obtained results show that the stability, vertical velocity, and boundary layer height do not significantly affect pollution levels. We identified some signals that are strongly related to PM2.5 high concentrations: weaker than average horizontal wind speed and TKE throughout the day, easterly winds in the morning (associated with the transport of ashes from wildfires produced on the Eastern plains), combined with a higher than average radiation peak. These results lead to a better understanding of the PM2.5 variations, which can be applied for the improvement of air quality models and have the potential to be part of a novel policy to manage air quality risk.

Section: Boundary-layer Height (Blh)mentioning

confidence: 56%

Section: Boundary-layer Height (Blh)mentioning

confidence: 99%

Understanding high polluted events in a tropical megacity with air quality observations and ERA5-reanalysis data

Rev.Fac.Ing.Univ.Antioquia

Córdoba²,

Sánchez-Cárdenas³

et al. 2022

Self Cite

Exposure risk to carbon monoxide concentrations inside a diesel-based bus rapid transport system: a CFD-Monte Carlo modeling approach

Guevara-Luna

Model. Earth Syst. Environ.

et al. 2023

Bus rapid transit (BRT) vehicles are common microenvironments in urban areas. In some cities, these BRT vehicles are diesel-powered, which makes them highly pollutant. Recent studies report high levels and exposure risk to particulate matter in BRT vehicles. Nevertheless, extensive research has yet to be published, including gaseous pollutants (e.g., CO). Nevertheless, extensive research including gaseous pollutants (e.g., CO) has not been published. This research aims to evaluate the self-pollution of BRT buses in terms of exhaust gasses. For this, measurements and computational fluid dynamics (CFD) were used. Results suggest that pollutant concentrations stay low during most of the trips. However, some areas of the buses have significant swings and peaks due to the transit cycle. Here, we used CFD modeling to evaluate the dispersion of the exhaust CO inside and outside the bus. CFD results show that the bus rear has the highest concentrations, with a mean self-pollution ratio of 12%. Additionally, we developed a method based on the source-receptor relationship to quantify the impact of exhaust emissions reduction on self-pollution, showing that the technological replacement of current diesel buses would reduce self-pollution and, therefore, passenger exposure. Finally, since modeling results may be inaccurate, an uncertainty analysis was developed using the Monte Carlo method to obtain a confidence interval of 90% for the variables linked to the self-pollution.

Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction

Jiménez-Saenz

Torres

et al. 2022

Sensors

Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps.