The San Luis Potosí valley is an endorheic basin that contains three aquifers: a shallow unconfined aquifer of alluvial material and two deep aquifers, free and confined. The groundwater contamination documented for the shallow aquifer generates contamination of the deep unconfined type aquifer, from which part of the population’s drinking water needs are met. This study records incipient anthropogenic contamination of two types: biogenic and potentially toxic trace elements. The studied contaminants include fecal coliform bacteria, total coliform, nitrate, and potentially toxic elements such as: manganese ($Mn$), mercury ($Hg$), arsenic ($As$), and cadmium ($Cd$). This contamination in some locations exceeds the permissible limit for human consumption. Some major consequences to health, including severe illness, may be caused by the trace elements. The present results give a first signal about the contamination of the deep unconfined type aquifer due to anthropogenic activity in the valley. This is a priority issue because this aquifer supplies drinking water, and in the short or medium term it will have an effect on public health.

A machine vision has been devised for determination of the size distribution of spherically shaped pellets in granular beds under static and dynamic conditions. This machine vision involved establishing the optimal distance between the illumination source and the top layer of the granular bed, as well as the development of the data image acquisition software and control systems, I/O interface hardware, and electromechanical system. The size distribution of pellets given by the whole system gives an error lower than 5% for pellets. The size distribution histogram or the Sauter diameter of the pellets given by the vision system is used as the input signal for controlling an electromechanical scanning device, which can be reliably used for automation tasks. The system software was developed for open access on a commercially widely used platform. As the machine vision is robust, it can be used in industrial environments and it has the potential to contribute in improving and optimizing pelletizing industrial processes. This machine vision is reliable, flexible, user friendly, inexpensive, and easy to implement.

For several decades the field of engineering geology, has had several difficulties to characterize properties of sedimentary particles. These particles are characterized by a multitude of sizes and shapes. For measuring the sizes there are a number of instruments based on different principles. However, the use of these instruments is not very widespread. The size of sediment particles covers several orders of magnitude therefore, the instruments have been developed do not meet all requirements. Several authors have demonstrated the feasibility to perform these tests by means of digital image processing. In this paper we propose to obtain optical particle size, morphological analysis and density, in a range of 30 microns to 50 mm. To this end we propose two assemblies that cover the range of sizes. In the proposed assembly is used as transport fluid particles. The fluid and allow a mechanism to disperse the sample, this solves the problem of segmentation in digital image processing. It uses a back-illuminated light for accurate measurement of particles due to the complexity of its component phases. By digital image processing can be 41 measurements on each particle. Furthermore allows morphological analysis using the Fourier method. A tracking algorithm to determine the density of the particles according to Stokes' law. Experiments demonstrate the accuracy and repeatability of this method compared with other methods of measurement.

Lava dome collapses often generate small volume pyroclastic density currents (PDCs) containing a proportion of dense, juvenile blocks derived from the source dome and crater rim/flanks, and a fines fraction comprising non-pumiceous ash. Due to practicalities of sampling and field equipment, grain size analyses are typically limited to particles below ~64 mm diameter, even though blocks of several metres in size can be found throughout the deposits. These poorly constrained large-block populations may contain key information about the collapse and transport processes. By examining the full grain size population of a PDC deposit at Volcán de Colima, we will develop a more complete understanding of the grain size distribution in dome collapse PDC deposits and evaluate the limitations intrinsic to common sampling strategies.Here we present granulometry that combines drone-derived and outcrop photographic image analysis, dry sieving, and lab-based optical granulometry to capture particle sizes across the entire particle size range. We show that when comparing grain size analyses which include blocks greater than 64 mm diameter there is a 4.97 mm difference in the mean grain size. We also provide a methodology for approximating the Sauter mean diameter of dome collapse PDC deposits where block populations have not been measured. Our findings highlight the importance of including the blocks in granulometry studies of PDC deposits. The blocks give key information on the flow conditions within the current, indicating a reduction in carrying capacity over time, and show the pulsatory behaviour of the PDCs.

Granular flows generate intense acoustic emissions, yet the quantitative relationship between particle size and sound frequency remains poorly characterized for natural, irregular materials. This study investigates the inverse relationship between mean particle size and the spectral centroid of acoustic emissions under controlled laboratory conditions. We conducted vertical-drop and inclined flume experiments using steel spheres as a control, alongside dacite and pumice sieved into narrow size fractions. Acoustic signals were acquired at a 96 kHz sampling rate and analyzed using the short-time Fourier transform (STFT) to compute spectral centroids. Vertical-drop experiments confirmed a robust inverse size–frequency trend across all materials, with spectral centroids decreasing systematically as particle diameter increased. Flume experiments preserved the general inverse relationship but exhibited greater variability and a systematic shift toward lower frequencies, indicating that boundary interactions and flow kinematics modulate the acoustic signature beyond pure particle size effects. The present work establishes a controlled physical basis for acoustics-based granulometric characterization of granular avalanches, as the first stage of a factor-isolation research program in which the particle size contribution is quantified under dry, single-phase conditions. Operational field deployment lies beyond the scope of this study; the results provide the controlled calibration baselines required before extensions to multiphase, field-scale flows can be meaningfully pursued and highlight the necessity of regime-specific calibration for confined flows.