Long-range transport (LRT) of air pollutants from a range of sources can substantially enhance background pollution levels, especially in urbanized regions, which can exacerbate high-pollution episodes. In the Aburrá Valley (AV), Colombia, and other cities in northern South America, biomass burning (BB), dust, and volcanic degassing have been identified as sources of long-range aerosol transport. However, the impact of these sources on air quality and the characterization of these sources have yet to be thoroughly studied. This work investigates the influence of these sources on the chemical composition of PM_2.5 during annual and intra-annual high-load aerosol events in the AV. We identified, tracked, and meteorologically characterized LRT events and evaluated their influence on PM_2.5 concentration and chemical composition. We found that the LRT of aerosols from BB, dust, and volcanic degassing influenced approximately 13 %, 8 %, and 13 % of days in the year, respectively. We applied the positive-matrix-factorization (PMF) statistical model to quantify PM_2.5 concentrations and chemical compositions for the different LRT event types (e.g., BB). For BB events, we identified large contributions from organic carbon (OC1 and OC2), F⁻, and secondary aerosol tracers (${{\mathrm{SO}}_{\mathrm{4}}}^{\mathrm{2}-}$ and ${{\mathrm{NO}}_{\mathrm{3}}}^{-}$ ). For dust LRT events, crustal mineral components, along with Ti and Ca, were the primary contributors to aerosol composition, while ${{\mathrm{SO}}_{\mathrm{4}}}^{\mathrm{2}-}$ , Na, Al, and Ca were the primary contributors during volcanic events. The concentrations of some ions and toxic heavy metals (Cr, Mn, Cd, and Ni) were also elevated during BB and volcanic-degassing events. BB contributed the most to PM_2.5 levels during the LRT events (∼11 µg m⁻³), while contributions from aerosols arising from dust and volcanic events were also substantial (<7 µg m⁻³). Our study identifies the Orinoco and the Middle Magdalena Valley as sizable sources of BB aerosols and Nevado del Ruiz as a source of volcanic aerosols. Additionally, we found that African dust reached the Andean region via the Caribbean route. As a result, we identify the need for future chemical-transport modeling studies in the region and new support strategies to manage internal and external pollution sources that degrade air quality in the AV and the surrounding region.

Environmental pollution indicated by the presence of $P{M}_{2.5}$ particulate matter varies based on prevailing atmospheric conditions described by certain meteorological variables. Consequently, it is important to understand atmospheric behavior in areas such as the Aburrá Valley, which experiences recurrent pollution events twice a year. This study examines the behavior of specific meteorological variables and $P{M}_{2.5}$ particulate matter in the Aburrá Valley. By using statistical analysis tools such as correlation coefficients, principal component analysis (PCA), and multiple linear regression models, the research identifies relationships between $P{M}_{2.5}$ and daily cycles of temperature, rainfall, radiation, and wind speed and direction. Datasets were analyzed considering periods before and after the COVID-19 lockdown (pre-pandemic and pandemic, respectively), and specific pollution events were also analyzed. Furthermore, this work considers the relationships between $P{M}_{2.5}$ and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with $P{M}_{2.5}$. There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show the opposite behavior. $P{M}_{2.5}$ exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between $P{M}_{2.5}$ levels and ABL height at different times of the day. Moreover, the PCA results show that the first principal component explains around 60% of the total variance in the hydrometeorological data. The second PC explains 10%, and the rest of the variance is distributed among the other three to eight PCs. In this sense, there is no significant difference between the two PCAs with hydrometeorological data from a pre-pandemic period and a COVID-19 pandemic period. Multiple regression analysis indicates a significant and consistent dependence of $P{M}_{2.5}$ on temperature and solar radiation across both analyzed periods. The application of Generalized Additive Models (GAMs) to our dataset yielded promising results, reflecting the complex relationship between meteorological variables and $P{M}_{2.5}$ concentrations. The metrics obtained from the GAM were as follows: Mean Squared Error (MSE) of 98.04, Root Mean Squared Error (RMSE) of 9.90, R-squared (R²) of 0.24, Akaike Information Criterion (AIC) of 110,051.34, and Bayesian Information Criterion (BIC) of 110,140.63. In comparison, the linear regression model exhibited slightly higher MSE (100.49), RMSE (10.02), and lower R-squared (0.22), with AIC and BIC values of 110,407.45 and 110,460.67, respectively. Although the improvement in performance metrics from GAM over the linear model is not conclusive, they indicate a better fit for the complexity of atmospheric dynamics influencing $P{M}_{2.5}$ levels. These findings underscore the intricate interplay of meteorological factors and particulate matter concentration, reinforcing the necessity for advanced modeling techniques in environmental studies. This work presents new insights that enhance the diagnosis, understanding, and modeling of environmental pollution, thereby supporting informed decision-making and strengthening management efforts.

Environmental pollution indicated by the presence of PM2.5 particulate matter varies based on prevailing atmospheric conditions described by certain meteorological variables. Consequently, it is important to understand atmospheric behavior in areas such as the Aburrá Valley, which experiences recurrent pollution events twice a year. This study examines the behavior of specific meteorological variables and PM2.5 particulate matter in the Aburrá Valley. By using statistical analysis tools such as correlation coefficients, principal component analysis (PCA), and multiple linear regression models, the research identifies relationships between PM2.5 and daily cycles of temperature, rainfall, radiation, and wind speed and direction. Data sets were analyzed considering periods before and after the COVID-19 lockdown (pre-pandemic and pandemic, respectively), and specific pollution events were also analyzed. Furthermore, this work considers the relationships between PM2.5 and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with PM2.5. There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show opposite behavior. PM2.5 exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between PM2.5 levels and ABL height at different times of the day. Moreover, the PCA results show that the first principal component explains around 60% of the total variance of the hydrometeorological data. The second PC explains 10%, and the rest of the variance is distributed among the other three to eight PCs. In this sense, there is no significant difference between the two PCA analyses with hydrometeorological data from a pre-pandemic period and a COVID-19 pandemic. Multiple regression analysis indicates a significant and consistent dependence of PM2.5 on temperature and solar radiation across both analyzed periods. This work presents new insights that enhance the diagnosis, understanding, and modeling of environmental pollution, thereby supporting informed decision-making and strengthening management efforts.

Integrating fire representation into climate models improves our understanding of ecosystem-fire-climate interactions by including connections between the carbon cycle and atmospheric composition. The Interactive Fires and Emissions algorithm for Natural Environments (INFERNO) is a new component of the UK Earth System Model (UKESM). Here, we evaluate carbon monoxide (CO) emissions from fires in South America as modelled by the INFERNO fire emissions model, which is coupled to the Joint UK Land Environment Simulator (JULES) in an offline configuration. Different satellite-based inventories were used for comparisons. To identify key factors driving simulated CO emissions and model-inventory biases, we use sensitivity experiments and a machine-learning approach. The findings indicate that INFERNO accurately capture the Arc of Deforestation in the southern Amazon as a primary source region of fire CO emissions, but it tends to overestimate these emissions by about 72 %. The simulated emission patterns in this region are largely determined by drought conditions and Plant Functional Type (PFT), particularly tree fractions. Aligned, the experiments show a 100 % increase in CO emissions in the southern Amazon region when using a drier meteorology dataset compared to the ERA5-based control run. In southern South America, INFERNO emissions, and in particular their seasonal cycle is affected by the tree PFT misrepresentation. The machine learning model explains 67 % of the model-inventory biases using only model inputs, highlighting room for improvement and the need to consider additional factors. The machine learning model identified soil moisture and tree PFT as major contributors to the model bias. Future model development should focus on improving the representation of fuel moisture, fuel load, and human activities (e.g., agriculture and deforestation) in the fire model.

Assessing long-range transport (LRT) of pollutants recognizes that multiple sources of varying scale and location can impact air quality. In the Aburrá Valley, Colombia, and other cities in Northern South America, biomass burning (BB), dust, and volcanic degassing have been identified as sources of LRT of aerosols. However, the impact of these sources on air quality and their characterization have yet to be thoroughly studied. This work investigates the influence of these sources on the chemical composition of PM_2.5 during annual and intra-annual high-load aerosol events. We identified, tracked, and meteorologically characterized LRT events and evaluated their influence on PM_2.5 concentration and chemical composition. We ran a Positive Matrix Factorization (PMF) for each kind of event, identifying high contribution in organic carbon (OC1, OC2), F^- and secondary aerosols trace SO₄^2- and NO₃^- for the BB events, crustal mineral along with Ti and Ca contribution for dust events and SO₄^2-, Na, Al and Ca for volcanic events. The increasing concentration of some ions and toxic heavy metals (Cr, Mn, Cd, and Ni) were also related to BB and volcanic degassing influence. During these LRT events, the BB fraction of PM_2.5 dominates by frequency and amount, averaging 11.14 μg / m³ (38 %). On average, dust and volcanic degassing contribute 6.77 μg / m³ (34 %) and 6.46 μg / m³ (30 %) of the concentrations. Of the three, dust events showed fewer affected days. The study highlighted hotspot zones such as the Orinoco and Middle Magdalena Valley for BB aerosols, the Caribbean for dust, and the Nevado del Ruíz volcano for volcanic aerosols. This study gives insights for future chemical transport modeling studies in the region and supports strategies to manage internal and external pollution sources and effects for the Aburrá Valley and the region.

Integrating fire simulation into climate models enhances our understanding of ecosystem-fire-climate interactions, clarifying the role of fire in the carbon cycle and other processes. The Interactive Fires and Emissions algorithm for Natural Environments (INFERNO) is one of the new modules in the upgraded UK Earth System Model (UKESM). Here, we use a version of INFERNO coupled only with the Joint UK Land Environment Simulator (JULES) to evaluate its performance and biases over South America (SA); a region that accounts for ∼15 % of global fire carbon emissions. For this, we compared carbon monoxide (CO) estimates from INFERNO (2004–2021) with five satellite-based biomass-burning inventories, conducted sensitivity experiments and developed a machine learning (ML) model targeting biases. INFERNO was able to represent CO emissions in most of the fire-active zone in SA, particularly the southern Amazon ’Arc of Deforestation’, but overestimates emissions (∼100 %) outside them (e.g. within the Amazon forest). The ML model (R² = 64 %) indicates that tree categories of Plant Functional Types (PFTs) and soil moisture— through its role in flammability and gross primary productivity (GPP) —significantly influence spatiotemporal biases. In northern SA, CO emissions were overestimated by approximately 300 % due to seasonal cycle inaccuracies, while INFERNO showed lower biases in southern SA emissions despite lacking seasonal representation. Both flammability and GPP underpinned the limited simulation of the seasonal cycle. Although INFERNO misrepresented emissions trends in the Arc of Deforestation, it successfully captured the increase in emissions in the eastern Andean Mountains from 2014 to 2021, albeit underestimating their magnitude. Sensitivity experiments revealed that the underlying PFT affected spatiotemporal variability (115 %) and trends (167 %) in CO emissions, while flammability influenced the seasonal cycle (116 %) and trends (158 %). These findings highlight the need for enhanced PFT accuracy and a deeper understanding of the roles of precipitation/soil moisture in GPP and flammability, as well as the consideration of landscape fragmentation to represent land management and forest fire vulnerability.