Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences.

The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m3, 10.2 µg/m3, and 7.3%, respectively.This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study's findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.

Evidence of human impact in Antarctic region by studying atmospheric aerosols.

Air quality is a global concerning topic because of its great impact on the environment and health. Because of that, the study of atmospheric aerosols looking for harmful pollutants is rising, as well as the interest in the origin of the contaminants. Depending on the nature and size of the aerosols, some elements can be detected at a great distance from the emission source, even in Antarctica, where this study is conducted. Several samples of PM filters from 2018 to 2019 (Deception Island) and 2019-2020 (Livingston Island) campaigns have been analyzed by three powerful spectroscopic techniques: FESEM (Field Emission Scanning Electron Microscopy), LIBS (Laser Induced Breakdown Spectroscopy), and ICP-MS (Inductively Coupled Plasma Mass Spectrometry). These techniques have allowed us to find some heavy metals in the air of the Antarctic region (Al, Fe, Ti, Ni, Cr, and Mn). Deeper studies on ICP-MS results have confirmed those results and have also provided information on their potential sources. Thus, while Al, Fe, Ti and Mn concentrations can be explained by crustal origin, Ni and Cr presented high values only coherent with important human contribution. The results point out that the Antarctic region is no longer a clean and isolated environment from human pollution.

Characterization of atmospheric aerosols in the Antarctic region using Raman Spectroscopy and Scanning Electron Microscopy.

The non-destructive spectroscopic characterization of airborne particulate matter (PM) was performed to gain better knowledge of the internal structures of atmospheric aerosols at the particle level in the Antarctic region, along with their potential sources. PM and soil samples were collected during the 2016-2017 austral summer season at the surroundings of the Spanish Antarctic Research Station "Gabriel de Castilla" (Deception Island, South Shetland Islands). PM was deposited in a low-volume sampler air filter. Raman spectroscopy (RS) and Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) were used to determine the elemental and molecular composition of the individual aerosol and soil particles. Filter spectra measured by these techniques revealed long-range atmospheric transport of organic compounds (polystyrene and bacteria), local single and cluster particles made of different kinds of black carbon (BC), exotic minerals (polyhalite, arcanite, niter, ammonium nitrate, syngenite and nitrogen, phosphorus, and potassium (NPK) fertilizer), and natural PM (sea salts, silicates, iron oxides, etc.). In addition to the filter samples, forsterite and plagioclase were discovered in the soil samples together with magnetite. This is the first report of the presence of a microplastic fiber in the Antarctic air. This fact, together with the presence of other pollutants, reflects that even pristine and remote regions are influenced by anthropogenic activities.

A real-world approach to identifying animal bones and Lower Pleistocene fossils by laser induced breakdown spectroscopy.

Archaeological sites often contain accumulations of remains derived from different independent events produced by different agents. Thus, in Palaeolithic sites, it is normal to find alternating occupations between humans and carnivores. The faunal assemblages at these sites usually include hundreds or thousands of bone fragments, which are very difficult to associate them to specific individuals since there are no currently available techniques able to do it in a straightforward and cost-effective way. In this work we present a methodology that allows us to characterise the anatomical remains of a bone accumulation and relate them all back to the specific individuals to which they belong. In order to provide a real world application, we have used a selection of animal bones from different individuals belonging to deer and sheep (fed in a controlled way using the same diet). On the other hand, fossilized faunal remains have also been analysed to verify if these fossilized bones keep some of the fingerprinting of the animal from which they come from. For this purpose, we have developed a protocol using Laser Induced Breakdown Spectroscopy (LIBS) together with Neural Networks (NN) implemented here to discriminate and reassemble deer and sheep bones from different individuals, which we subsequently applied for these proposes to fossilized material. To the best of our knowledge, this is the first time that this technique has been applied for individual fingerprinting to actuality and fossil samples. The elemental composition of bones provides enough information to get a correct discrimination of different individuals. The spectral correlation has exceeded 95 %. and all individuals were correctly classified to the individual from which they come from. There have been no instances of false positives or false negatives in our tests or applications.

Aerosol analysis by micro laser-induced breakdown spectroscopy: A new protocol for particulate matter characterization in filters.

Atmospheric aerosols (particulate matter - PM) affect the air quality and climate, even in remote areas, such as the Antarctic Region. Current techniques for continuous PM monitoring are usually complex, costly, time consuming and do not provide real-time measurements. In this work, based on micro laser-induced breakdown spectroscopy (LIBS), an innovative method with an optical design and multi-elemental scanning imaging, is presented to characterize PM collected in filters from Antarctica. After following a simple protocol and under atmospheric pressure, the new approach allows to obtain a global visualization of the elemental PM composition of the filters with a minimum sample destruction and preparation. For the first time, we were able to map the localization of pollutants in filters at high spatial resolution and speed. This recent method offers a new insight on the characterization of PM, particularly in isolated areas, where no complex equipment and real time measurements are demanded.

Statistical Tools for Air Pollution Assessment: Multivariate and Spatial Analysis Studies in the Madrid Region.

The present work reports the distribution of pollutants in the Madrid city and province from 22 monitoring stations during 2010 to 2017. Statistical tools were used to interpret and model air pollution data. The data include the annual average concentrations of nitrogen oxides, ozone, and particulate matter (PM10), collected in Madrid and its suburbs, which is one of the largest metropolitan places in Europe, and its air quality has not been studied sufficiently. A mapping of the distribution of these pollutants was done, in order to reveal the relationship between them and also with the demography of the region. The multivariate analysis employing correlation analysis, principal component analysis (PCA), and cluster analysis (CA) resulted in establishing a correlation between different pollutants. The results obtained allowed classification of different monitoring stations on the basis of each of the four pollutants, revealing information about their sources and mechanisms, visualizing their spatial distribution, and monitoring their levels according to the average annual limits established in the legislation. The elaboration of contour maps by the geostatistical method, ordinary kriging, also supported the interpretation derived from the multivariate analysis demonstrating the levels of NO2 exceeding the annual limit in the centre, south, and east of the Madrid province.

Plume Dynamics of Laser-Produced Swine Muscle Tissue Plasma.

We report on the plume dynamics of the plasma induced by laser ablation of a swine skeletal muscle tissue sample in different vacuum conditions. Pulses from a transversely excited atmospheric CO2 laser were focused onto a target sample and the induced plasma was allowed to expand in different air pressures. The expansion features were studied using fast photography of the overall visible emission by using a gated intensified charged coupled device. Free expansion and plume splitting were observed at different pressure levels. The expansion of the plasma plume front was analyzed using various expansion models and the velocity of the plume front was estimated. The effect of the number of accumulated laser shots on the crater volume at different ambient air pressures and an elemental analysis of the sample were performed using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (EDX) analysis. The surface morphology of the irradiated surface showed that increasing the pressure of the ambient gas decreased the ablated mass, or in other words it reduced significantly the laser-target coupling.

Application of laser-induced breakdown spectroscopy (LIBS) and neural networks to olive oils analysis.

The adulteration and traceability of olive oils are serious problems in the olive oil industry. In this work, a method based on laser-induced breakdown spectroscopy (LIBS) and neural networks (NNs) has been developed and applied to the identification, quality control, traceability, and adulteration detection of extra virgin olive oils. Instant identification of the samples is achieved using a spectral library, which was obtained by analysis of representative samples using a single laser pulse and treatment by NNs. The samples used in this study belong to four countries. The study also included different regions of each country. The results obtained allow the identification of the oils tested with a certainty of more than 95%. Single-shot measurements were enough for clear identification of the samples. The method can be developed for automatic real-time, fast, reliable, and robust measurements, and the system can be packed into portable form for non-specialist users.

Neural network analysis of spectroscopic data of lycopene and beta-carotene content in food samples compared to HPLC-UV-vis.

In this study a neural network (NN) model was designed to predict lycopene and beta-carotene concentrations in food samples, combined with a simple and fast technique, such as UV-vis spectroscopy. The measurement of the absorbance at 446 and 502 nm of different beta-carotene and lycopene standard mixtures was used to optimize a neural network based on a multilayer perceptron (MLP) (learning and verification process). Then, for validation purposes, the optimized NN has been applied to determine the concentration of both compounds in food samples (fresh tomato, tomato concentrate, tomato sauce, ketchup, tomato juice, watermelon, medlar, green pepper, and carrots), comparing the NN results with the known values of these compounds obtained by analytical techniques (UV-vis and HPLC). It was concluded that when the MLP-NN is used within the range studied, the optimized NN is able to estimate the beta-carotene and lycopene concentrations in food samples with an adequate accuracy, solving the UV-vis interference of beta-carotene and lycopene.

Solving the spectroscopy interference effects of beta-carotene and lycopene by neural networks.

In this study a new computerized approach and linear models (LMs) to solve the UV/vis spectroscopy interference effects of beta-carotene with lycopene analysis by neural networks (NNs) are considered. The data collected (absorbance values) obtained by UV/vis spectrophotometry were transferred into an NN-trained computer for modeling and prediction of output. Such an integrated NN/UV/vis spectroscopy approach is capable of estimating beta-carotene and lycopene concentrations with a mean prediction error 50 times lower than that calculated by the LM/UV/vis spectroscopy approach (without any previous physicochemical knowledge of the process to be modeled).