Search Results (20)

The Latin American and Caribbean (LAC) region faces persistent challenges of inequality, climate change vulnerability, and deteriorating air quality. The Aburrá Valley, where Medellín is located, is a narrow tropical valley with complex topography, strong thermal inversions, and unstable atmospheric conditions, all of which exacerbate the accumulation of pollutants. In Medellín, ${\mathrm{NO}}_2$ concentrations have remained nearly unchanged over the past eight years, consistently approaching critical thresholds, despite the implementation of air quality control strategies. These persistent high concentrations are closely linked to the variability of the atmospheric boundary layer (ABL) and are often intensified by prolonged dry periods. This study focuses on a representative street canyon in Medellín that has undergone recent urban interventions, including the construction of new public spaces and pedestrian areas, without explicitly considering their impact on ${\mathrm{NO}}_x$ dispersion. Using Computational Fluid Dynamics (CFD) simulations, this work evaluates the influence of urban morphology on ${\mathrm{NO}}_x$ accumulation. The results reveal that areas with high Aspect Ratios (AR > 0.65) and dense vegetation exhibit reduced wind speeds at the pedestrian level—up to 40% lower compared to open zones—and higher ${\mathrm{NO}}_2$ concentrations, with maximum simulated values exceeding 50 μg/m³. This study demonstrates that the design of pedestrian corridors in complex urban environments like Medellín can unintentionally create pollutant accumulation zones, underscoring the importance of integrating air quality considerations into urban planning. The findings provide actionable insights for policymakers, emphasizing the need for comprehensive modeling and field validation to ensure healthier urban spaces in cities affected by persistent air quality issues. Full article

Medellín, a densely populated city in the Colombian Andes, faces significant health and environmental risks due to poor air quality. This is linked to the atmospheric dynamics of the valley in which it is located (Aburrá Valley). The region is characterized by a narrow valley and one of the most polluted areas in South America. This is a comparative study of the chemical composition of PM_2.5 (particles with diameter less than 2.5 µm) in Medellín between two periods (2014–2015 and 2018–2019) in which temporal trends and emission sources were evaluated. PM_2.5 samples were collected from urban, suburban, and rural stations following standardized protocols and compositional analyses of metals (ICP-MS), ions (ion chromatography), and carbonaceous species (organic carbon (OC) and elemental carbon (EC) by thermo-optical methods) were performed. The results show a reduction in average PM_2.5 concentrations for the two periods (from 26.74 µg/m³ to 20.10 µg/m³ in urban areas), although levels are still above WHO guidelines. Urban stations showed higher PM_2.5 levels, with predominance of carbonaceous aerosols (Total Carbon—TC = OC + EC = 35–50% of PM_2.5 mass) and secondary ions (sulfate > nitrate, 13–14% of PM_2.5 mass). Rural areas showed lower PM_2.5 concentrations but elevated OC/EC ratios, suggesting the influence of biomass burning as a major emission source. Metals were found to occupy fractions of less than 10% of the PM_2.5 mass; however, they included important toxic species associated with respiratory and cardiovascular risks. This study highlights progress in reducing PM_2.5 levels in the region, which has been impacted by local policies but emphasizes current and future challenges related mainly to secondary aerosol formation and carbonaceous aerosol emissions. Full article

A multivariable clustering methodology was evaluated using the LAMDA algorithm as an alternative tool for analyzing air quality data. This analysis was based on the assessment of marginal and global adequacy degrees for classification using temporal records of PM_2.5 data. This study was conducted before and during the COVID-19 pandemic in the Aburrá Valley, Colombia. A total of 244 samples were collected between 1 December 2018, and 23 November 2020, over 24-h periods at a frequency of three days per week, including weekends. A robust classifier was developed for the PM_2.5 dataset, demonstrating that the selected descriptors significantly influenced classification outcomes. The average value for each class fell within the established ranges of the air quality index (AQI). According to AQI scales, the “good” and “acceptable” categories accounted for 95.1% of the monitored days. Class C2 (“acceptable”) was the most prevalent, representing 66% of the records, while the category harmful to sensitive groups (4.5%) was observed in eleven instances. Additionally, only one record (0.4%) fell into the category harmful to health (C4). The proportions of C1 and C2 classifications before and during the pandemic were 93.7% and 97.7%, respectively. The improvement in air quality due to COVID-19 restrictions is evident, as 57% of the observations during the pandemic were classified as “good” (C1), compared to only 13.9% before the pandemic. The visualization of classification results through easily interpretable graphs serves as a valuable decision-making tool, integrating not only real-time PM_2.5 measurements but also historical trends of the study area. Full article

Canine distemper virus (CDV) is the causative agent of a widespread infectious disease affecting both domestic and wild carnivores. Owing to its ability to cross species barriers and its high fatality rate in unvaccinated animals, CDV poses a significant conservation threat to endangered wildlife worldwide. To date, two distinct CDV lineages have been reported in Colombia, with cases documented separately in domestic dogs and wild peri-urban carnivores. This study aimed to detect and characterize the concurrent circulation of CDV in naturally infected domestic dogs and crab-eating foxes (Cerdocyon thous) from the same area in Colombia. Through molecular and phylogenetic analyses, we identified the South America/North America-4 lineage infecting both populations simultaneously. Our findings revealed high genetic variability, multiple virus reintroductions, and a close relationship with CDV strains previously detected in the United States. These results confirm the simultaneous circulation of CDV in the domestic and wildlife interface and underscore the urgent need for an integrated approach to CDV prevention and control involving both domestic and wildlife health interventions. Full article

The increase in yearly particulate matter concentrations has been a constant issue since 2017 in the Aburrá Valley, located in Antioquia, Colombia. Although local certified air quality monitors provide high accuracy, they are limited in spatial coverage, limiting chemical transport and pollution dynamic studies in this mountainous environment. In this work, a local, Low-Cost Sensor network is proposed as an alternative and has been installed around the valley in representative locations and heights. To calibrate PM_2.5 and O₃ sensors used by the network, temporal delays were analyzed with Dynamic Time Warping and the linear scale was corrected with a Single Linear Regression model. As a result, the correlation coefficient R² of the sensor reached values of 0.8 and 0.9 after calibration. For all network stations, rescaled data agrees with official historical reports on the behavior of pollutant concentrations and meteorological variables. The ability to compare the network results with certified data confirms the success of the calibration/validation method employed and contributes to the growing field of low-cost air quality sensors in Latin America. Full article

The Aburrá Valley, located in the northwestern region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography and dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning and sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics. The kurtosis calculations highlighted areas where infrequent but significant changes occurred, such as large-scale construction projects or sudden shifts in land use, providing a statistical measure of surface variability throughout the study period. An advanced clustering technique segmented images into distinctive classes, utilizing fuzzy logic and a kernel-based method, enhancing the analysis of changes. Additionally, Pearson correlation coefficients were calculated to explore the relationships between identified land cover change classes and their spatial distribution across nine distinct geographic zones in the Aburrá Valley. The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management and environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting and understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation. Full article

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. Full article

This study aims to identify the most polluted areas and sites using the magnetic signal of ornamental plant leaves as an indicator of environmental pollution. Systematic sampling was conducted with 98 sampling sites described according to urban land use, such as road hierarchy and road surface, soil group, collected plant species, and municipality. The magnetic parameters analyzed were low- and high-frequency magnetic susceptibility and the isothermal remanent magnetization acquisition curves in order to calculate the magnetic enhancement factor. For the analysis of variance, a Kruskal–Wallis test was performed to compare urban land uses. Subsequently, the magnetic enhancement factor in dust and surface soil was used to prepare maps of environmental pollution for each urban area. Analyses of the different magnetic parameters of the dust deposited on leaves show that low-coercivity ferrimagnetic minerals dominated the magnetic signal, probably magnetite of anthropic origin, and were closely linked to vehicular traffic and, to a lesser extent, industrial activities. Full article

This study presents a conceptual framework proposal for integrating urban freight initiatives (UFIs), or city logistics initiatives, into urban planning and urban management (UPUM) land use and infrastructure systems. As a novel approach, this framework integrates three components: Firstly, a conceptual basis on three economic geography theory principles—location, agglomeration, and urbanisation. Secondly, spatial analysis and subsequent clustering integrate companies’ spatial positions, their proximity to other companies, their freight intensity, and the characteristics of the zonal road infrastructure; these clusters are defined as freight traffic zones (FTZs). Thirdly, a functional yet strategic UFI clustering or grouping is proposed to work in an optimised and integrated manner with the FTZs’ opportunities for efficiency and reduced externalities. It is expected that the integrated result of these three components can serve to optimise freight initiatives and road infrastructure from a city governance perspective, reduce freight externalities, and function as a stakeholder cooperation tool from government-led, policy-driven perspectives. This research also identifies and characterises various variables influencing the emergence and existence (planned or organic) of FTZs and shows how these could be incorporated into high-level UPUM processes. Although it is deemed that the principles and methodological approach followed here could be common to urban areas, an example for the Metropolitan Area of the Aburra Valley (MAAV), in Colombia, is presented as an initial case study. Conclusively, this paper introduces a pioneering methodology for integrating UFIs into city or metropolitan governance, offering guidance for policymakers to promote sustainable freight systems. Full article

This article presents a comprehensive genetic study focused on pre-Hispanic individuals who inhabited the Aburrá Valley in Antioquia, Colombia, between the tenth and seventeenth centuries AD. Employing a genetic approach, the study analyzed maternal lineages using DNA samples obtained from skeletal remains. The results illuminate a remarkable degree of biological diversity within these populations and provide insights into their genetic connections with other ancient and indigenous groups across the American continent. The findings strongly support the widely accepted hypothesis that the migration of the first American settlers occurred through Beringia, a land bridge connecting Siberia to North America during the last Ice Age. Subsequently, these early settlers journeyed southward, crossing the North American ice cap. Of particular note, the study unveils the presence of ancestral lineages from Asian populations, which played a pivotal role in populating the Americas. The implications of these results extend beyond delineating migratory routes and settlement patterns of ancient populations. They also enrich our understanding of the genetic diversity inherent in indigenous populations of the region. By revealing the genetic heritage of pre-Hispanic individuals from the Aburrá Valley, this study offers valuable insights into the history of human migration and settlement in the Americas. Furthermore, it enhances our comprehension of the intricate genetic tapestry that characterizes indigenous communities in the area. Full article

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. Full article

The densest network for measuring air pollutant concentrations in Colombia is in Medellin, where most sensors are located in the heavily polluted lower parts of the valley. Measuring stations in the higher elevations on the mountains surrounding the valley are not available, which limits our understanding of the valley’s pollutant dynamics and hinders the effectiveness of data assimilation studies using chemical transport models such as LOTOS-EUROS. To address this gap in measurements, we have designed a new network of low-cost sensors to be installed at altitudes above 2000 m.a.s.l. The network consists of custom-built, solar-powered, and remotely connected sensors. Locations were strategically selected using the LOTOS-EUROS model driven by diverse meteorology-simulated fields to explore the effects of the valley wind representation on the transport of pollutants. The sensors transmit collected data to internet gateways for posterior analysis. Various tests to verify the critical characteristics of the equipment, such as long-range transmission modeling and experiments with an R score of 0.96 for the best propagation model, energy power system autonomy, and sensor calibration procedures, besides case exposure to dust and water experiments, to ensure IP certifications. An inter-calibration procedure was performed to characterize the sensors against reference sensors and describe the observation error to provide acceptable ranges for the data assimilation algorithm (<10% nominal). The design, installation, testing, and implementation of this air quality network, oriented towards data assimilation over the Aburrá Valley, constitute an initial experience for the simulation capabilities toward the system’s operative capabilities. Our solution approach adds value by removing the disadvantages of low-cost devices and offers a viable solution from a developing country’s perspective, employing hardware explicitly designed for the situation. Full article

We estimate the seroprevalence of IgG antibodies to varicella zoster virus (VZV) based on the first serological study in a cohort of pregnant women and newborns from the Aburrá Valley (Antioquia-Colombia) who attended delivery in eight randomly chosen hospitals. An indirect enzyme immunoassay was used to determine anti-VZV IgG antibodies. Generalized linear models were constructed to identify variables that modify seropositivity. In pregnant women, seropositivity was 85.8% (95% CI: 83.4–85.9), seronegativity was 12.6% (95% CI: 10.8–14.6), and concordance with umbilical cord titers was 90.0% (95% CI: 89–91). The seropositivity of pregnant women was lower in those who lived in rural areas (IRR: 0.4, 95% CI: 0.2–0.7), belonged to the high socioeconomic status (IRR: 0.4, 95% CI: 0.2–0.7), and had studied 11 years or more (IRR: 0.6, 95% CI: 0.4–0.8). Among newborns, seropositivity was lower in those who weighed less than 3000 g (IRR: 0.8, 95% CI: 0.6–1.0). The high seropositivity and seronegativity pattern indicates the urgent need to design preconception consultation and vaccination reinforcement for women of childbearing age according to their sociodemographic conditions, to prevent infection and complications in the mother and newborn. Full article

This article presents an indicator weighting method for constructing composite indices to assess sustainable development at the subnational level. The study uses an analytic hierarchy process (AHP), which is considered relevant, since it establishes links between the indicators that make up the different sustainable development goals (SDG). For this purpose, 28 indicators defined by experts constitute the base to evaluate the progress towards sustainable development of the Aburrá Valley region, located in Antioquia, Colombia. The results show that health, employment, and education indicators obtained higher weights, while environmental indicators received the most reduced weights. Likewise, the model proves to be consistent using a consistency ratio, which generates the possibility of replicating this model at different subnational levels. Full article

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM_2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation. Full article