David Riveros-Rosas

El secado solar, es una alternativa sustentable para la preservación de frutas; sin embargo, la exposición a la radiación ultravioleta (UV) afecta la calidad y estabilidad de los compuestos bioactivos. El objetivo de este estudio fue evaluar el efecto de la radiación UV en la conservación de compuestos bioactivos de la fresa (Fragaria × ananassa) deshidratada mediante secadores solares directos, con cubiertas de acrílico y policarbonato, operados por convección natural y forzada, comparándolos con un secador de aire caliente a 60 °C. Los experimentos se realizaron en dos periodos contrastantes: agosto de 2024 y febrero de 2025. Se determinaron humedad, actividad de agua, cambio de color (ΔE), contenido fenólico total, antocianinas totales, ácido ascórbico y radiación UV incidente dentro de los secadores. Los resultados mostraron que todos los métodos lograron productos microbiológicamente estables (humedad <10% y aw <0.6). Los secadores de policarbonato presentaron menor transmitancia de radiación UV, favoreciendo una mayor retención de antocianinas y ácido ascórbico, así como, menor alteración de color. El contenido fenólico total se incrementó tras el secado. Se concluye que el uso de policarbonato como cubierta en secadores solares directos mejora significativamente la calidad nutricional y funcional de la fresa deshidratada.

A new daily composite of the solar flare index (SFI) and the hemispherically‐resolved versions (hSFI) are presented for 1937 to 2024. The data set confirms that the northern hemisphere (NH) dominated solar flare activity during Solar Cycles 17 to 21, but that the southern hemisphere has dominated from Solar Cycle 22 to present. That said, the highest SFI value occurred in the NH during the recent superstorm of May 2024. In sunspot activity, the “Gnevyshev‐Ohl rule” shows that the sum of sunspot numbers during even‐numbered cycles is related to those of adjacent odd‐numbered cycles. A similar rule appears to apply to SFI. The “Gnevyshev gap” phenomenon where solar maximum activity sometimes has two peaks separated by up to 1–2 years of a gap is confirmed for SFI. Although our data set represents the longest continuous daily data set for solar flare activity to‐date, it is known that stronger solar flare events occurred before 1937. Therefore, a brief discussion of earlier solar flare events in the historical record is also provided for context. The statistics of the SFI and hSFI series are compared to other solar and geomagnetic activity indices, including the May and October 2024 solar storms. Statistical analysis of past geomagnetic storms confirms they are more frequent during active cycles and less frequent during solar minima. Strong geomagnetic storms are also more likely to occur during the positive phase of a 1.7 year's quasi‐biennial oscillation in solar activity. The likelihood of low‐magnetic latitude aurorae seems to have a 30 year periodicity component.

This study investigates the seasonal variability of solar radiation and aerosol optical properties in southern Mexico, using high-resolution measurements from the SEL station of the Baseline Surface Radiation Network (BSRN) during the 2023–2024 period. The results indicate that global horizontal irradiance (GHI) and direct normal irradiance (DNI) reach maximum values during the dry season, particularly in February, March, and April. In contrast, diffuse horizontal irradiance (DHI) increases during the rainy season, reflecting enhanced cloud cover. Comparison with the ESRA model revealed a strong correlation, with a slight overestimation under clear-sky conditions. AERONET observations showed peak aerosol optical depth (AOD) values in April and May, coinciding with biomass burning events. Elevated Ångström exponent (α) values during this period indicate the predominance of fine-mode particles. In the wet season, both AOD and α decreased, likely due to wet deposition and a transition toward coarser aerosol types. Radiative forcing (ΔF) estimates revealed a net warming effect at the surface during the dry season, with a maximum ΔF at the bottom of the atmosphere (BOA) of 106.7 W/m² in May. In contrast, aerosols produced a cooling effect at the top of the atmosphere (TOA), with ΔF reaching –38.1 W/m², underscoring their dual role in the regional energy balance. Satellite-derived emissions of black carbon (BC) and PM₂.₅ showed strong consistency with AOD and ΔF patterns, confirming biomass burning as a dominant aerosol source. These results enhance our understanding of aerosol–radiation interactions in tropical regions and support improvements in climate modeling and solar energy planning.

The assessment of solar resources involves the utilization of physical or satellite models for the determination of solar radiation on the Earth’s surface. However, a critical aspect of model validation necessitates comparisons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges associated with establishing and maintaining solar radiation measurement networks—characterized by their expense, logistical complexities, limited station availability and the imperative consideration of climatic criteria for siting—countries endowed with substantial climatic diversity face difficulties in station placement. In this investigation, the measurements of annual solar irradiation, from meteorological stations of the National Weather Service in Mexico, were compared in different regions clustered by similarities in altitude, TL Linke, albedo and cloudiness index derived from satellite images; the main objective is to find the best ratio of annual solar irradiation in a set of clusters. Employing machine learning algorithms, this research endeavors to identify the most suitable model for predicting the ratio of annual solar irradiation and to determine the optimal number of clusters. The findings underscore the efficacy of the L-method as a robust technique for regionalization. Notably, the cloudiness index emerges as a pivotal feature, with the Random Forest algorithm yielding superior performance with a R² score of 0.94, clustering Mexico into 17 regions.

This paper presents an analysis of the morphology and columnar microphysical properties of atmospheric aerosols in Mexico City (MC) for the period 2022–2023. The morphological study focused on the structure description of soot particles and tar balls (TB). By transmission electron microscope (TEM) and scanning electrode microscope (SEM), voluminous soot aggregates mixed with TBs were observed. The chemistry shows that both soot and TBs are mostly carbonaceous species with well-defined morphologies. On the other hand, the columnar aerosol microphysical properties recovered from AERONET show that the particles have a bimodal aerosol size distribution (ASD) with two modes: fine and coarse. The ASD remains constant without showing significant seasonal changes, only with some variability for coarse particles. The aerosol optical depth (AOD) value is significantly high, typical of urban areas. The real (n) and imaginary (k) parts of the complex refractive index (CRI) were obtained from the photometric measurements. The CRI values show seasonal variations, with spring being the season with the highest values for n, while the highest values for k were measured in winter.

Concentrated solar energy systems, particularly Concentrated Solar Power (CSP), need to capture a significant amount of Direct Normal Irradiance (DNI) and demand high precision in forecasting their availability in the near future. Understanding how DNI intensity fluctuates across its path through the atmosphere down to the Earth’s surface is crucial for harnessing solar energy effectively, as atmospheric conditions define the amount of irradiance that can be captured by CSP systems. To enhance operational efficiency, accurate DNI forecasts are required for short, medium, and long-term durations. In this paper, we present a model that automates two independent studies based on previously published papers by the authors and on sky camera images and solarimetric data. These studies estimate DNI attenuation caused by cloudiness using an artificial neural network (ANN) and measure the optical flow in sky images with the Lucas-Kanade method to forecast the future position of clouds. The proposed model aims to predict DNI in the short-term (5, 10, 15, 20, 25, and 30 minutes) for the entire year of 2021, under various sky conditions (overcast, partially cloudy, and cloudless). The results demonstrate an annual correlation coefficient ranging from r = 0.90 to r = 0.87 in the forecasting horizon from 5 to 30 minutes.

The radiation flux distributions produced by the concentrating solar systems used to produce thermal/electrical power are usually non-homogeneous. This results in non-uniform temperature distributions on the solar receivers, causing adverse effects on the system’s overall performance. An approach to better understand the problem is to study the surfaces around the focal zone where the radiation density is homogeneous (isosurfaces), generating them from experimental data. For this, it is necessary to superimpose built volumes of the different irradiance levels using parallel planes in different directions from the focal point of a concentrator. These volumes are known as effective volumes. This study presents the model used to generate effective volume produced by a point focus concentrator, comparing it with experimental results in a direction perpendicular to the focal axis. The effective volumes were developed considering a global optical error of the system of 2.8 mrad. The set of methods used to generate effective volumes has not been previously presented in the literature. The theoretical-experimental research consisted of the combination of the camera-target method and the simulations by the ray-tracing technique. The results showed effective volumes with the highest value of 10 MW/m² and the lowest value of 4.5 MW/m².

The atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajectory in real time is one of the functions and tools that CSP operators must develop for plant optimization, and to anticipate drops in solar irradiance. For short forecast of cloud movement (10 min) is enough with describe the cloud advection while for longer forecast (over 15 min), it is necessary to predict both advection and cloud changes. In this paper, we present a model that predict only the cloud advection displacement trajectory for different sky conditions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. In the short term, up to 10 min the future position of the cloud front is predicted with 92% certainty while for 25–30 min, the best predicted precision was 82%.