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Pollen forecasting models are a useful tool with which to predict episodes of type I allergenic risk and other environmental or biological processes. Parietaria is a wind-pollinated perennial herb that is responsible for many cases of severe pollinosis due to its high pollen production, the long persistence of the pollen grains in the atmosphere and the abundant presence of allergens in their cytoplasm and walls. The aim of this paper is to develop artificial neural networks (ANNs) to predict airborne Parietaria pollen concentrations in the northwestern part of Spain using a 19-year data set (1999–2017). The results show a significant increase in the length of time Parietaria pollen is in the air, as well as significant increases in the annual Parietaria pollen integral and mean daily maximum pollen value in the year. The Neural models show the ability to forecast airborne Parietaria pollen concentrations 1, 2, and 3 days ahead. A developed model with five input variables used to predict concentrations of airborne Parietaria pollen 1 day ahead shows determination coefficients between 0.618 and 0.652.

Transit data analysis and artificial neural networks (ANNs) have proven to be a useful tool for characterizing and modelling non-linear hydrological processes. In this paper, these methods have been used to characterize and to predict the discharge of Lor River (North Western Spain), 1, 2 and 3 days ahead. Transit data analyses show a coefficient of correlation of 0.53 for a lag between precipitation and discharge of 1 day. On the other hand, temperature and discharge has a negative coefficient of correlation (−0.43) for a delay of 19 days. The ANNs developed provide a good result for the validation period, with R2 between 0.92 and 0.80. Furthermore, these prediction models have been tested with discharge data from a period 16 years later. Results of this testing period also show a good correlation, with R2 between 0.91 and 0.64. Overall, results indicate that ANNs are a good tool to predict river discharge with a small number of input variables.

Some biological particles present in the atmosphere, such as pollen grains, give rise to human health problems, allergies, and infections. In view of the recognized special allergenic ability of Plantago pollen grains, a model based on an artificial neural network (ANN) was developed in this work in order to forecast the Plantago airborne pollen concentration. The proposed model uses data from Plantago pollen and the main meteorological variables recorded during 16 years (1993–2008) in the city of Ourense (northwest Spain). Its accuracy was tested during the years 2009 and 2010 with a prediction horizon of 2 days in advance. The model was applied in the atmosphere of the city of Ourense (Spain). Obtained results show that ANN model provides good results against other classical mathematical methodologies, which do not convergence so well. The forecasted pollen concentrations here are applied to allergology because they allow taking into account preventive measures in risk pollinosis suffers population.

Different hot springs and boreholes in the city of Ourense, Galicia, Spain, have been studied to determine the mineral equilibrium conditions of the discharged groundwaters and the reservoir temperatures predicted by the equilibrium conditions. Ourense is located in the Miño River’s valley. The area is characterized by two fault systems, which determine groundwater circulation. A NW trending fault system is the permeable system that transfers groundwater and heat to springs in the Miño River valley as it is evident from the location of the springs in the region. Groundwaters traveling and discharging from granitic and schistose rocks are mainly bicarbonate waters. In comparison, groundwaters traveling and discharging from granodiorite rocks can be bicarbonate, sulfate or chloride waters. Different equilibrium activity diagrams for the dominant cations in groundwater (Na + , K + , Ca 2+ , Mg 2+ ) have been constructed to correlate water equilibrium conditions with the mineral assemblage, K-feldspar, clinochlore, muscovite, quartz, and calcite. Granites and schists are the rocks within which groundwater circulation approaches mineral equilibrium with equilibrium temperatures around 140–160 °C. Groundwaters circulating throughout granodiorite seem a little bit high in Mg 2+ to reach equilibrium conditions. Miño River’s tectonic valley presents strong morphological contrasts in terms of faulting and rock types to the north of the river that allow increased longitude and depth of groundwater circulation. These conditions allow close achievement of water–mineral equilibrium conditions. More research is needed to know the extension of this energy resource and optimize its use.

Reservoirs play a key role in many human societies due to their capability to manage water resources. In addition to their role in water supply and hydropower production, their ability to retain water and control the flow makes them a valuable asset for flood mitigation. This is a key function, since extreme events have increased in the last few decades as a result of climate change, and therefore, the application of mechanisms capable of mitigating flood damage will be key in the coming decades. Having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. When historical data are available, data-driven models have been proven a useful tool for different hydrological applications. In this sense, this study analyzes the efficiency of different machine learning techniques to predict reservoir outflow, namely multivariate linear regression (MLR) and three artificial neural networks: multilayer perceptron (MLP), nonlinear autoregressive exogenous (NARX) and long short-term memory (LSTM). These techniques were applied to forecast the outflow of eight water reservoirs of different characteristics located in the Miño River (northwest of Spain). In general, the results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches such as to consider reservoir outflow equal to that of the previous day. Among the different machine learning techniques analyzed, the NARX approach was the option that provided the best estimations on average.

In recent years, the stability of carbamate pesticides have been studied by our research group in a wide range of biomimetic microheterogeneous media such as micelles or reverse micelles. These microheterogeneous media included different surfactant species and, hence, different self-assembled structures. In particular, basic hydrolysis of carbofuran and its derivatives have been analysed in the presence of anionic, cationic, non-ionic and reverse micelles. The results obtained from these physicochemical and kinetic studies, as well as a consistent comparison of them, are now summarised.

Aggregation numbers of micelles based on N-alkylamines and mixed systems CTACl/N-alkylamines have been determined using fluorescence techniques. The values of aggregation number are compared as a function of hydrocarbon chain length and as a function of the molar fraction in the mixed systems.

This review reports on the alkaline fading of crystal violet and other related carbocations in the presence of different microheterogeneous media (micelles, microemulsions and vesicles).

In recent years our reseach group has investigated the chemical behaviour of β-cyclodextrin (CD)/surfactant mixed systems and their characteristics as reaction media. The results have been interpreted in terms of a pseudophase model that takes into account the formation of both CD-surfactant and CD-substrate complexes and also, in some cases, the exchange of X - and OH - ions between the micellar and aqueous pseudophases. from the experimental results it was concluded that the presence of CD has no effect on existing micelles but raises the critical micellar concentration (cmc). on the other hand, at surfactant concentrations above the cmc, competition between the micellisation and complexation processes leads to the existence of a significant concentration of free CD in equilibrium with the micellar aggregates. The percentage of uncomplexed β-CD in equilibrium with the micellar system increases on increasing the hydrophobicity of the surfactant molecule. This behaviour was justified taking into account the existence of two simultaneous processes: complexation of surfactant monomers by CD and the process of self-assembly to form micellar aggregates. The autoaggregation of surfactant monomers is more important than the complexation process in this mixed system. Varying the hydrophobicity of the surfactant monomer enabled us to determine that the percentages of uncomplexed CD in equilibrium with the micellar system were in the range of 5-95%. When the surfactant self-assembly structure is a vesicle, the free CD in the CD/surfactant mixed system yields a percentage of 100%.

Microemulsions are highly versatile reaction media, which currently find many applications. In this review, we shall describe recent trends in the use of microemulsions as organic reaction media, and present models for their functioning, in particular the pseudophase model. This model allows a quantitative explanation of organic reactivity in these microheterogeneous media.