Chemical transport models (CTM) are crucial for simulating the distribution of air pollutants, such as particulate matter, and evaluating their impact on the environment and human health. However, these models rely heavily on accurate emission inventory and meteorological inputs, usually obtained from reanalyzed weather data, such as the European Centre for Medium-Range Weather Forecasts (ECMWF). These inputs do not accurately reflect the complex topography and micro-scale meteorology in tropical regions where air pollution can pose a severe public health threat. We propose coupling the LOTOS–EUROS CTM model and the weather research and forecasting (WRF) model to improve LOTOS–EUROS representation. Using WRF as a meteorological driver provides high-resolution inputs for accurate pollutant simulation. We compared LOTOS–EUROS results when WRF and ECMWF provided the meteorological inputs during low and high pollutant concentration periods. The findings indicate that the WRF–LOTOS–EUROS coupling offers a more precise representation of the meteorology and pollutant dispersion than the default input of ECMWF. The simulations also capture the spatio-temporal variability of pollutant concentration and emphasize the importance of accounting for micro-scale meteorology and topography in air pollution modelling.

Governments’ responses to the COVID-19 pandemic provide a unique opportunity to study the effects of restricted socioeconomic activity on air quality. Here, we study the changes in air pollution levels during the lockdown in Medellín and its metropolitan area, Colombia, for periods with and without enhanced regional fire activity, considering the effects of meteorology using random forest and multiple linear regression methods. The lockdown measures, which reduced mean traffic volume by 70% compared to 2016–2019, resulted in reductions for PM_2.5 (50–63%), PM₁₀ (59–64%), NO (75–76%), NO₂ (43–47%), and CO (40–47%), while O₃ concentration increased by 19–22%. In contrast, when fire activity was high, the effects of the lockdown on air quality were shadowed by the long-range transport of biomass burning emissions, increasing fine particulate matter and ozone. This study shows that healthier levels are achievable through significant efforts from decision-makers and society. The results highlight the need to develop integral measures that do not only consider reductions in the local emissions from transportation and industry, but also the role of fire activity in the region, as well as the difficulties of achieving reductions in ozone from measures that are effective at reducing primary pollutants.

Mesoscale convective systems (MCSs) are crucial in shaping large‐scale tropical circulation and the hydrological cycle, particularly in Northwestern South America (NwSA), a region marked by complex terrain and significant MCS activity. Understanding MCSs in NwSA is vital due to their impact on precipitation patterns and potential for severe weather events. To enhance this understanding, the ATRACKCS algorithm was developed for tracking convective systems, utilizing precipitation and brightness temperature data sets. This research focuses on documenting the spatiotemporal variability of MCS occurrence, life cycle, and movement. Notably, MCS hotspots were identified to the west of the major orographic features in the region, with maximum occurrences at night, contrasting with the region's typical afternoon peak in land convection. MCS movement is also heavily influenced by topography, with higher velocities on the eastern (windward) side of the Andes compared to velocities on the western (leeward) side. MCSs generally move westward, driven by easterly winds, but this pattern is not consistent throughout the year or region. Northward movement is predominant to the west of the Andes, while southward movement is observed to the east. These seasonal and regional movement variations are linked to factors such as the intertropical convergence zone position, moisture availability, topography, and low‐level jets. This research underscores the complexity of MCSs in NwSA and emphasizes the need for detailed studies on the atmospheric environment shaping these systems. Additionally, it provides a robust 21‐year MCS database for NwSA and an advanced tracking tool for research in various geographic contexts and impact areas.

With the aim of understanding the impact of air pollution on human health and ecosystems in the tropical Andes region (TAR), we aim to couple the Weather Research and Forecasting Model (WRF) with the chemical transport models (CTM) Long-Term Ozone Simulation and European Operational Smog (LOTOS–EUROS), at high and regional resolutions, with and without assimilation. The factors set for WRF, are based on the optimized estimates of climate and weather in cities and urban heat islands in the TAR region. It is well known in the weather research and forecasting field, that the uncertainty of non-linear models is a major issue, thus making a sensitivity analysis essential. Consequently, this paper seeks to quantify the performance of the WRF model in the presence of disturbances to the initial conditions (IC), for an arbitrary set of state-space variables (pressure and temperature), simulating a disruption in the inputs of the model. To this aim, we considered three distributions over the error term: a normal standard distribution, a normal distribution, and an exponential distribution. We analyze the sensitivity of the outputs of the WRF model by employing non-parametric and robust statistical techniques, such as kernel distribution estimates, rank tests, and bootstrap. The results show that the WRF model is sensitive in time, space, and vertical levels to changes in the IC. Finally, we demonstrate that the error distribution of the output differs from the error distribution induced over the input data, especially for Gaussian distributions.