Mining companies used to abandon tailing heaps in countryside regions of Mexico and other countries. Mine residues (MRs) contain a high concentration of potentially toxic elements (PTE). The wind can disperse dust particles (<100 μm) and once suspended in the atmosphere, can be ingested or inhaled; this is a common situation in arid climates. Nowadays, there is little information on the risk of exposure to PTEs from particulate matter dispersed by wind. The pseudo-total PTE in bulk and fractionated MR after aqua regia digestion, the inhalable bioaccessibility with Gamble solution (pH = 7.4), and the gastric bioaccessibility with 0.4 M glycine solution at pH 1.5 were determined. As and Pb chemical species were identified by X-ray absorption near-edge structure (XANES) spectroscopy. The highest rate of dispersion was observed with 74–100 μm particles (104 mg m⁻² s⁻¹); in contrast, particles <44 μm had the lowest rate (26 mg m⁻² s⁻¹). The highest pseudo-total As (35 961 mg kg⁻¹), Pb (3326 mg kg⁻¹), Cd (44 mg kg⁻¹) and Zn (up to 4678 mg kg⁻¹) concentration was in the <20 μm particles and As in the 50–74 μm (40 236 mg kg⁻¹) particles. The highest concentration of inhaled bioaccessible As (343 mg kg⁻¹) was observed in the <20 μm fraction and the gastric bioaccessible As was 744 mg kg⁻¹, Pb was 1396 mg kg⁻¹, Cd was 19.2 mg kg⁻¹, and Zn was 2048 mg kg⁻¹. The predominant chemical As species was arsenopyrite (92%), while 54% of Pb was in the adsorbed form. Erodible particle matter is a potential risk for humans in case of inhalation or ingestion.

The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Methodologies have been proposed for the study of crops. Most of them require field survey for ground truth data and a single crop map is generated for the whole season at the end of the crop cycle and for the next crop cycle a new field survey is necessary. Here, we present models for recognizing maize (Zea mays L.), beans (Phaseolus vulgaris L.), and alfalfa (Medicago sativa L.) before the crop cycle ends without current-year field survey for ground truth data. The models were trained with an exhaustive field survey at plot level in a previous crop cycle. The field surveys begin since days before the emergence of crops to maturity. The algorithms used for classification were support vector machine (SVM) and bagged tree (BT), and the spectral information captured in the visible, red-edge, near infrared, and shortwave infrared regions bands of Sentinel 2 images was used. The models were validated within the next crop cycle each fifteen days before the mid-season. The overall accuracies range from 71.9% (38 days after the begin of cycle) to 87.5% (81 days after the begin cycle) and a kappa coefficient ranging from 0.53 at the beginning to 0.74 at mid-season

El proceso de formación del suelo está determinado por la interacción de seis factores de formación: clima, organismos, topografía, material parental, tiempo y uso, la magnitud de cualquiera de las propiedades del suelo está determinada por la acción de estos factores. En este trabajo se determinaron cuáles son los factores de formación que influyen en el tipo y propiedades del suelo en la cuenca rancho Dolores en la región mixteca en el estado de Oaxaca. Se realizó un análisis de componentes principales y una clasif icación no supervisada para la delimitación de unidades de suelo, en las que se realizaron muestreos y análisis de laboratorio de algunas propiedades físicas y químicas. Los factores de formación que explicaron la variabilidad de los suelos de la cuenca fueron: pendiente, índice de posición topográf ica, material parental y uso del suelo. Se delimitaron cinco unidades de suelos con su distribución espacial en la cuenca y se asociaron con sus propiedades de textura, densidad aparente, capacidad de campo, punto de marchitez permanente, porciento de saturación, conductividad hidráulica saturada, pH y contenido de materia orgánica. Existe una fuerte relación entre los parámetros del relieve y los suelos, por lo que se recomienda el estudio de la formación, distribución, y cartografía de los suelos desde un punto de vista geomorfológico, así como del análisis de la vegetación.

The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran’s I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.

Mexico’s basin is one of the most altered in the country, owing to the presence of the megalopolis of Mexico City. Lake Texcoco, which had the basin’s biggest extension, dried up almost completely. The basin’s evolution over time led in the formation of a megabasin in which water is transported from one source to another to serve the urban region and subsequently drained to prevent flooding. The major hydrotechnical works in Mexico Basin have been interpreted as a solution to the problem of flooding in Mexico City, but they were actually part of a much larger strategy of territorial appropriation by the Spanish colonists. The ecological imbalance that has resulted has sparked a variety of social issues. For the purpose of analyzing the environmental transformation of Lake Texcoco over the last 500 years, actors and processes that influenced specific moments in the country’s history were identified; these elements showed the inexorable relationship between the lake and Mexico City. Subsequently, they were grouped by periods with similar trends in terms of the way in which society relates to and appropriates the natural environment of the lake. It was found that the critical moment for the desiccation of Lake Texcoco occurred during the Spanish colonial historical period as part of the redesign of the city; from then on, the same environmental imaginary prevailed century after century, shaped by social and economic factors. This study contributes to the literature on how urbanization affects natural resources by making an original theoretical contribution through an analysis based on political ecology, and it adds to the literature on how people use the prevailing federal area of the lake.