Air pollution analysis in Northwestern South America: A new Lagrangian framework.

This study examines the spatiotemporal variations of PM, PM, SO, O, NO, and NO concentrations in Northwestern South America (NWSA). We assess the efficacy of existing policies, identify underlying phenomena, and highlight areas for further research. Significant findings have emerged by analyzing reanalysis and in-situ data, employing the WRF-Chem model, and utilizing a new Lagrangian framework designed to overcome some drawbacks common to analysis of pollution Long-Range Transport.

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach.

Unlabelled: 2020 presented the ideal conditions for studying the air quality response to several emission reductions due to the COVID-19 lockdowns. Numerous studies found that the tropospheric ozone increased even in lockdown conditions, but its reasons are not entirely understood. This research aims to better understand the ozone variations in Northern South America.

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

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.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM, PM, PMC (coarse fraction of PM), NO, SO, NOx, CO, O and the total gaseous oxidant (OX = NO + O) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020.

Validation of PM and PM early alert in Bogotá, Colombia, through the modeling software WRF-CHEM.

Air quality data from Bogotá, Colombia, show high levels of particulate matter (PM), which often generate respiratory problems to the population and a high economic cost to the government. Since 2016, air quality in the city of Bogotá has been measured through the Bogota Air Quality Index (IBOCA) which works as an indicator of environmental risk due to air pollution. However, available technological tools in Bogotá are not enough to generate early alerts due to PM and PM.

Compassion-focused imagery reduces shame and is moderated by shame, self-reassurance and multisensory imagery vividness.

Compassion-focused imagery (CFI) is an emotion-regulation technique involving visualization of a person, animal or object offering one compassion, to generate feelings of safeness. It is proven to increase self-compassion and reduce negative affect. This study explores two hypotheses not previously investigated: i) which sensory modalities can stimulate compassionate affect; and ii) whether presentation of pictorial stimuli can enhance CFI.