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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. [ABSTRACT FROM AUTHOR]

Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in Salgar (Colombia). A cascading modeling methodology was developed, integrating the spatially distributed rainfall intensities, hazard zoning with the SLIDE model, propagation modeling with RAMMS using calibrated soil rheological parameters, the distributed hydrological model TETIS, and flood mapping with IBER. Return periods of 2.33, 5, 10, 25, 50, and 100 years were defined and applied throughout the methodology. A specific extreme event (18 May 2015) was modeled for calibration and comparison. The spatial rainfall intensities indicated maximum concentrations in the northwestern upper basin and southeastern lower basin. Six landslide hazard maps were generated, predicting landslide-prone areas with a slightly above random prediction rate for the 2015 event. The RAMMS debris flow modeling involved 30 simulations, indicating significant deposition within the river channel and modifying the terrain. Hydraulic modeling with the IBER model revealed water heights ranging from 0.23 to 7 m and velocities from 0.34 m/s to 6.98 m/s, with urban areas showing higher values, indicating increased erosion and infrastructure damage potential. [ABSTRACT FROM AUTHOR]

Long-term average river discharges as well as peak and low flows of different return periods are estimated along the entire river network of Colombia, through the conjoint use of the long-term water balance in the river basins and the framework of statistical scaling, taking the average flow field as the scaling variable. Estimation of the long-term water balance considers the spatial variability of hydrologic fields, in which drainage basins are considered the basic hydrological control volumes for estimation. A systematic effort has been made to estimate the long term average precipitation field combining rain gauge measurements with existing handmade expert maps as an input trend for a universal Kriging interpolation technique. Evaluation of estimates for actual and potential long-term evapotranspiration was implemented using diverse methods. Results were tested using the long term water balance equation against 200 streamflow gauging stations. No method for actual evapotranspiration showed significant superiority. Overall, we conclude that the magnitude of errors arises fundamentally from deficiencies in the data and the sparsity of the observations. [ABSTRACT FROM AUTHOR]