The Spanish education system is undergoing a profound transformation due to the implementation of a new education law. In this work, we will make a comparison between the old and new educational laws about the content related to Geology in primary education. Moreover, the Spanish curriculum will be compared to the New Generation Science Standards (NGSS), since this is the reference curriculum used by the largest American research institutions (NASA, USGS, NPS, Universities, ...). This study will analyze the specific curriculum that will be implemented in the Canary Islands (Spain). In this sense, we will pay special attention to the treatment of the volcanic phenomenon and the use of the Canarian geological resources. The Geology of the XXI century is characterized by its interdisciplinarity, where subjects such as Physics, Mathematics, Chemistry, Ethics or Engineering play a fundamental role. Is this taken into account in the new education law? Finally, we will offer a series of didactical tips to give students and teachers of primary education a broader, engaging and motivating vision of Geology that awakens their curiosity towards Earth Sciences.

In recent decades, heavy metal contamination in soils has received a significant concern on a global scale. Quantitative apportionment of heavy metal sources in the surface soil throughout extensive regions is a perplexing task. This study indicated a receptor model to evaluate the heavy metal concentrations of various sources for the soil and the related contamination impacts. In this study, the surface soil at the Cerrito Blanco in San Luis Potosi, Mexico was chosen as the case study location to reveal the potential pollution sources of heavy metals. The research suggested the combined use of the positive matrix factorization (PMF) model for quantitative assessment of contamination sources and spatial distribution techniques for the conception of pollution sources, which forms the foundation for later soil contamination control and treatment. Throughout the study region, a total of thirty-nine samples of surface soil were collected. However, the mean concentration levels in the soils of Co, Cr, Cu, Ni, and Pb were lower than the permissible standards for soils. But it may be concluded that heavy metals have been contaminated in the surface soil since the mean concentration levels of As and Cd was higher than their permissible standard values by around 5.43 and 1.19 times, respectively. The PMF findings demonstrate that four major diverse sources of heavy metals in this study area's soils were natural, past mining activities, groundwater, and human activities. The concentrations of heavy metals in surface soils were considerably influenced by natural activities, which were generally the main factors. The spatial distribution of soil contamination for heavy metals was also mapped using the Geographic Information System (GIS) technique. As a result, more focus will be required to assess the heavy metal contamination of soils in regions that have had increasing economic growth and prosperity, such as Mexico.

Cities and towns are growing rapidly because of increasing population, which is not always contributing to the growth and prosperity of cities and towns, but also contribute to their associated problems, like waste generation and its management. This is directly questioning the sustainability of urbanization. On the other, the quantity of waste is increasing with increasing population, is a serious issue which requires proper planning. A number of processes are involved for management of solid waste, like siting, monitoring, collection, transportation, processing and disposing. However, foremost after collection of solid waste, a site is required for disposing and processing. The present study attempts to use Remote Sensing and GIS coupled with AHP method for analyzing and identifying a dumping Site for solid waste disposal in Nagercoil Municipality in Tamil Nadu. The different physical criteria: LU/LC, geomorphology, soil texture, settlement, road, surface water bodies, lineaments, groundwater depth, NDVI and slope are considered and are examined in relation to land fill site selection. Each criterion was identified and weighted by AHP score and mapped using GIS technique and suitable map is prepared by overlay analysis. The result indicates that a barren land of 57.8891 sq. km which is also proximity to Nagercoil city is the best site for disposal of solid waste.

Debris-flow Flume experiments of deposition and fan formation have been mostly conducted over hard non-porous plane. However, the surface a debris-flow travels on is influencing the dynamics of the flow and the deposition process as it has been shown recently. We have argued that (1) water exchange occurs with the surface and (2) material exchange (erosion and deposition) is also occurring in the deposition area. Continuing from this set of experiments, we are now attempting to clarify the role of debris in the debris-flow, one of them which are often neglected is wood debris.

In the present contribution, the authors attempted to quantify the role of wood debris on deposition and deposits in a controlled environment. For the present experiment, we used a flume with a reception pan that is 185.6 cm length x 95.6 cm width, with walls 26.2 cm high. The channel is 400 cm long, 25.0 cm high, and 18.5 cm wide. Repeating earlier experiments without wood debris, the receiving pan was spread with a 1-2 cm thick sediment layer. In the channel, a set of wood debris were erected. From this setup, the authors ran multiple experiment setups with 5 repeats of the same experiment each time. Using video-camera recording of the flow, photogrammetry of the deposits. The authors also collected sediment samples over the surface to compare porosity and density of the deposit.

The results have shown that the wood debris increased the variability in density and porosity in the deposit, regardless of the starting setting with woods, in comparison to “no-wood” patterns. The authors argue that this is due to mixing with the wood debris during deposition, modifying the semi-linear energy loss on a typical deposition fan. The mixing is further complicated as wood debris collided with each other based on the video analysis, so that the variability is not only due to the characteristics of the debris, but how it interacted with other debris and with the sediments. The authors further argue that the difference in erosion of the substratum, with wood-debris rich experiments tended to erode less, may be due to reduced momentum due to the energy consumed to transport the wood debris.

In the last decade, rainfall radar has been deployed at volcanoes like Mt. Merapi in Indonesia, and can even cover a whole country like in Japan, where the X-Rain (eXtended Radar Information network) product has been available for local research. However, the linkage between raingage data and radar spatial data (over a 250 m x 250 m grid) still presents discrepancies, and these challenges are particularly accurate in regions of high local-topographic variations like at Mount Unzen in Japan. As the volcano is over the Shimabara peninsula, it is surrounded by the sea with topography locally rising to 1,483 m.

To improve the forecast and to better understand the triggering mechanisms of lahars (volcanic debris-flows) at Mount Unzen, quantifying the spatial distribution of rainfalls is essential, and as a first step it is important to first understand how data taken locally by rain-gages relate to the radar data. Because empirical models have not been able to show any clear correlation, the present contribution has been developing a neural-network with two hidden layers that takes into account the rainfall per hour, the temperature and the wind speed and direction. The model takes a logistic activation function and the loss function is optimized using the Mean Squared Errors and the Mean Absolute Error. The choice of the activation function and the optimizer is the result of running several combinations of optimization functions with different activation functions. Once the best fit was chosen, the sigmoid with a SGD (Stochastic Gradient Descent) optimizer was chosen, and when training the model for 120 cycles, Shimabara station and the XRain data shows an error < 4 mm rainfall, while at the Unzen summit, even after 300 cycles, the validation error remained at 8 mm while the training loss was < 4mm. This shows that location specific functions might be necessary for each location, not only taking into account the weather data, but also the local topographic variability and the topographic position on slopes. Finally, neither the raingage, nor the Xrain are “true data” and the remaining error can also be the result of an error existing in any of the two dataset. Furthermore, the X-Rain data are spatialized data over an area, whereas the raingage is very limited in terms of spatial representativity.