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.

In this study, the climate change, tsunami and biodiversity for 336 km coastline endangered at the South China Sea was investigated with the review for the past, current and prediction models for the future. The hydraulic study of the coastal area was conducted using a well-established 2D numerical model suite Delft3D. The study revealed that the generated earthquakes at the convergence zone in the last century are small (Mw7.3), the possibility that a megathrust earthquake event in the SCS basin occurs in the future. The study area comprises a narrow strip of vegetation notably dominated by Casuarina equisetifolia with other coastal plants. Mangrove forests are found along the coastline and estuaries that are overlaid with marine alluvial soils. The current paper is the first comprehensive study of the South China Sea, and the findings increase the awareness among the public to understand the risk associated with environmental pollution.

We investigated the accumulation of Zn, Cu, Pb, and Cd in the soil and the leaves and bark of five common tree species (Eucalyptus occidentalis Endl., Acacia salicina Lindl., Cupressus sempervirens L., Casuarina equisetifolia L., and Tamarix aphylla (L.) Karst.) in the city of Gabès Tunisia to elucidate their bioaccumulation potential and determine their usefulness as biomonitors of metallic pollution in arid urban areas. Our results indicated that the bark had higher mean concentrations of Pb and Cd than leaves. In contrast, the leaves had higher mean concentrations of Zn and Cu than bark. No hyperaccumulation was detected for any of the analyzed metals in any of the studied species. E. occidentalis and T. aphylla had the highest mean concentrations of the investigated metals in leaves and bark. Based on the calculated metal accumulation index (MAI) values, these two species accumulated more metals than other studied tree species. Likewise, the concentrations of Zn, Cu, Pb, and Cd in soil had significant positive correlations with that in leaves and bark. Accordingly, E. occidentalis could be used for biomonitoring in arid areas subjected to industrial and traffic pollution. T. aphylla would be a good alternative when native species are a priority.