Carlos Alberto Palacio Tobón

Coastal vulnerability is a critical determinant of risk in littoral systems, particularly in regions where seasonal environmental forcing intersects with persistent socioeconomic fragility. This study assesses the integrated socioeconomic and environmental vulnerability (SEVI) of 18 microzones along the Urabá coast in the Colombian Caribbean. Grounded in the exposure-sensitivity-adaptive capacity framework, the methodology utilizes a seasonally explicit approach to differentiate vulnerability patterns between dry and wet periods over a 30-year climatological baseline. The Environmental Vulnerability Sub-Index (EVI) incorporates variables such as significant wave height and precipitation, while the Socioeconomic Vulnerability Sub-Index (SVI) employs census-derived proxies for demographic pressure and structural precarity. The results reveal a clear alongshore gradient, with the northern sector exhibiting consistently high vulnerability due to direct open-sea exposure and urban settlements with precarious conditions. Analysis shows that while environmental stressors alternate in dominance, wave energy in the dry season and precipitation in the wet season, the overall spatial configuration of vulnerability remains remarkably stable. This persistence underscores the structuring role of socioeconomic conditions, such as high population density and limited adaptive capacity (socioeconomic stratum 1), in governing coastal risk. Sensitivity analysis confirms the robustness of the SEVI rankings across different weighting schemes. These findings highlight that effective climate change adaptation in the Greater Caribbean must transcend hazard mitigation to address the underlying social determinants of vulnerability. The proposed microzone-based framework provides a transferable and parsimonious decision-support tool for spatially targeted coastal management and risk-informed land-use planning in data-limited regions.

Context: The injuries caused by falls-from-height (FFH) are a significant public health concern. FFH is one of the most common causes of polytrauma. The injuries persist to be significant adverse events and a challenge regarding injury severity assessment to identify patients at high risk upon admission. Understanding the incidence and the factors that predict injury severity can help in developing effective intervention strategies. Artificial intelligence (AI) predictive models are emerging to assist in clinical assessment with challenges. Objectives: This retrospective study investigated the incidence of FFH injuries utilizing conventional statistics and a predictive AI model to understand the fall-related injury profile and predictive factors. Methods: A total of 124 patients who sustained injuries from FFHs were recruited for this retrospective study. These patients fell from a height of 15–30 feet and were admitted into a level II trauma center at the border of US-Mexica region. A chart review was performed to collect demographic information and other factors including Injury Severity Score (ISS), Glasgow Coma Scale (GCS), anatomic injury location, fall type (domestic falls vs. border wall falls), and comorbidities. Multiple variable statistical analyses were analyzed to determine the correlation between variables and injury severity. A machine learning (ML) method, the multilayer perceptron neuron network (MPNN), was utilized to determine the importance of predictive factors leading to in-hospital mortality. The chi-square test or Fisher’s exact test and Spearman correlate analysis were utilized for statistical analysis for categorical variables. A p value smaller than 0.05 was considered to be statistically different. Results: Sixty-four (64/124, 51.6 %) patients sustained injuries from FFHs from a border wall or fence, whereas 60 (48.4 %) sustained injuries from FFHs at a domestic region including falls from roofs or scaffolds. Patients suffering from domestic falls had a higher ISS than border fence falls. The height of the falls was not significantly associated with injury severity, but rather the anatomic locations of injuries were associated with severity. Compared with border falls, domestic falls had more injuries to the head and chest and longer intensive care unit (ICU) stay. The MPNN showed that the factors leading to in-hospital mortality were chest injury followed by head injury and low GCS on admission. Conclusions: Domestic vs. border FFHs yielded different injury patterns and injury severity. Patients of border falls sustained a lower ISS and more lower-extremity injuries, while domestic falls caused more head or chest injuries and low GCS on admission. MPNN analysis demonstrated that chest and head injuries with low GCS indicated a high risk of mortality from an FFH.

The carbonaceous particles represent a significant fraction in the particulate matter (PM) and are considered an environmental hazard due to their effects on climate and health. The main goal in this research is to identify and analyze the scope that have been achieved so far on the characterization and measurement of the carbonaceous fraction present in PM, a great contribution to global pollution and thus to the deterioration of public health. The ProKnow-C methodology was used to build a bibliographic portfolio and perform a bibliometric and systemic analysis of the information found in the chosen databases. The contribution of these carbonaceous compounds to PM is very significant, reaching values up to 50%. The most used methods for the determination of organic and elemental carbon are thermo-optical reflectance and transmittance. Positive Factorization models are used worldwide to determine potential sources of particulate matter emissions. Even though various studies have been developed to understand these carbonaceous substances, there are several limitations in the measurements and limited knowledge on the subject. The positive outcomes and future possibilities were analyzed as well.

Particulate matter (PM), particle pollution that can travel long distances, is a big concern because it contains liquid droplets or microscopic solids resulting in significant health issues such as respirational and cancer problems. Therefore, the characterization of these particles is very significant as a hazard to public health. PM can be identified by X-ray diffraction (XRD) and Raman spectroscopy (RS), both powerful and non-destructive technologies. RS, in particular, allows the identification of black carbon, considered one of the pollutants with the greatest influence on climate change. Another important technology for the evaluation of inorganic and organic functional groups present in PM compounds is the Fourier transform infrared spectroscopy (FTIR). X-ray fluorescence (XRF) provides elemental analysis, revealing, in many cases, the original source of the sample. In order to understand the current state of the art, the Proknow-C method was applied to track the most recent information on PM characterization. Aspects such as sample collection, filter material, characterization parameters, PM components, and the advantages and limitations of each technique are discussed. PM minerals are found to be composed of silicates, oxides, sulfates, and carbonates. The elemental components of PM are classified into five categories: marine aerosol, mineral material, anthropogenic elements, organic carbon, and elemental carbon. The XRD technique is a powerful, fast, and non-destructive tool to identify various minerals present in PM. On the other hand, the XRF technique requires minimal sample treatment, but its sensitivity is limited for the determination of trace metals and some relevant environmental elements. FTIR spectroscopy is able to identify and quantify all organic functional groups present in atmospheric PM. Despite its advantages, a proper choice of calibration method is crucial to ensure its effectiveness. RS is fast and simple, although it only detects Raman-active functional groups. These are some of the advantages and limitations of these techniques addressed in the following review article.

This work describes a water quality model for heavy metals simulation in river systems. The proposed strategy comprises an 1D modeling approach with an ADZ-QUASAR extension to represent the behavior of heavy metals. This methodology seeks to strengthen their predictive capability based on the integration of variables which play an important role in the adsorption and desorption of these particles. The methodology was implemented in a reach of the Negro river (eastern side of Antioquia, Colombia), using Chromium, Copper and Nickel as heavy metals, because these are the heavy metals representative of the currents in the study area. Results are showing, with some degree of uncertainty, the capacity of the methodology to predict the behavior of environmental interesting substances, which makes it an important management tool.

Mental health situation in Colombia

Transit-oriented Development in the metropolitan governance: a comparison between the Colombian case and the South Korean experience

Se presenta la estructura metodológica del curso “Proyectos de Ingeniería Integrados a la Comunidad” (PIIC), como parte de una estrategia de la Facultad de Ingeniería de la Universidad de Antioquia para efectuar las directrices de su plan de desarrollo en cuanto a la implementación de procesos innovadores en la enseñanza de la Ingeniería. Con esta metodología se responde también a la necesidad de acercar a los estudiantes a la comunidad y ofrecerle a ambos frentes (estudiantes y comunidad) la oportunidad de participar en proyectos de ingeniería en todas sus fases, a través de la aplicación de técnicas pedagógicas basadas en la resolución de problemas, trabajo multidisciplinario, conformación de grupos multinacionales y multiculturales y el uso de las TIC. Se desglosan los distintos proyectos realizados hasta la fecha y se destaca la importancia de la creación de redes y el apoyo institucional en el éxito de este tipo de iniciativas.

Water quality modeling intends to represent a water body in order to assess their status and project the effects of different measures taken for their protection. This paper presents the results obtained from the Qual2kw model implementation in the first 50 kilometers of the Aburrá-Medellín River, in their most critical conditions of water quality, which correspond to low flow rates. After the model calibration, three recovery scenarios (short-term, medium-term and long-term) were evaluated. In the first scenario the sanitation only improved in some streams, in accordance with the Plan of Sanitation and Management of Discharges that was considered. Medium and long-term scenarios, with the operation of the new Water Waste Treatment Plant (WWTP) of the Bello municipality and an increase in the sewage collection, were considered. The obtained results show the positive impact of the operation of the WWTP of Bello in the balance of BOD5, dissolved oxygen and nitrogen.

Urban valleys can experience serious air pollution problems as a combined result of their limited ventilation and the high emission of pollutants from the urban areas. Idealized simulations were analyzed to elucidate the breakup of an inversion layer in urban valleys subject to a strong low-level temperature inversion and topographic effects on surface heating such as topographic shading, as well as the associated air pollution transport mechanisms. The results indicate that the presence and evolution in time of the inversion layer and its interplay with an urban heat island within the valley strongly influence the venting of pollutants out of urban valleys. Three mechanisms of air pollution transport were identified. These are transport by upslope winds, transport by an urban heat island–induced circulation, and transport within a closed slope-flow circulation below an inversion layer.