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Vehicular traffic occupies a significant place among the sources of air pollution, due to population and urban growth that has led to an excessive increase in the vehicle fleet worldwide, and in Costa Rica as well. Vehicle emissions generate greenhouse gases (GHGs), particulate matter (PM), and heavy metals (HMs), due to combustion products from fossil-fuel engines, tire wear, and brake linings. HMs are important because they cannot be degraded or destroyed naturally; however, they can be diluted by physicochemical agents and be incorporated into trophic chains where they can be bioaccumulated causing significant negative effects on human well-being and ecological quality. This study aimed to assess the HM pollution load in biomonitors and road dust from vehicular emissions by chemical analyses and magnetic properties modeling. For this purpose, chemical and magnetic property analyses were carried out on samples of road dust and leaves of Cupressus lusitanica Mill. and Casuarina equisetifolia L., which were sampled during 2 different years in the Greater Metropolitan Area of Costa Rica known as GAM. Contamination factor (CF) and pollution load index (PLI) results showed significant metal pollution in some of the study sites. Contamination by the metals V, Cr, and Zn was most commonly present in the biomonitors, and for road dust, they were Cr, Zn, and Pb. The PLI estimates obtained with the validated support vector machine (SVM) magnetic properties models were consistent (sensitivity, specificity, and precision) with those obtained by chemical analysis, demonstrating the feasibility of this method for the identification of this index of contamination. [ABSTRACT FROM AUTHOR]

Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R2 values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R2 values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors , yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R2 values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied. [Display omitted] • Magnetic properties-heavy metal levels in biomonitors and road dust were studied. • SVM is the best model for estimating heavy metal levels in leaves and road dust. • Casuarina equisetifolia models well the deposited Fe, Cu, Cr, V and Zn pollution. [ABSTRACT FROM AUTHOR]

This paper introduces a new spark plasma sintering technique that is able to order crystalline anisotropy by in-series/in situ DC electric coupled magnetic field. The process control parameters have been investigated on the production of anisotropic BaFe12O19 magnets based on resulted remanence (Mr). Sintering holding time (H.T.), cooling rate (C.R.), pressure (P), and sintering temperature (S.T.) are optimized by Taguchi with L9 orthogonal array (OA). The remanent magnetization of nanocrystalline BaFe12O19 in parallel (Mrǁ) and perpendicular (MrꞱ) to the applied magnetic field was regarded as a measure of performance. The Taguchi study calculated optimum process parameters, which significantly improved the sintering process based on the confirmation tests of BaFe12O19 anisotropy. The magnetic properties in terms of Mrǁ and MrꞱ were greatly affected by sintering temperature and pressure according to ANOVA results. In addition, regression models were developed for predicting the Mrǁ as well as MrꞱ respectively. [ABSTRACT FROM AUTHOR]