Detecting changes in industrial pollution by analyzing heavy metal concentrations in tree-ring wood from Romanian...

The impact of air pollution on forests, especially in urban areas, has been increasingly discussed recently. Many pollutants, including heavy metals, are released into the atmosphere from various sources, such as mining, non-ferrous metal processing plants, and fossil fuel combustion. These pollutants can adversely affect not only tree growth but also other species, including humans. This study compared the concentrations of several elements in tree-ring wood from two conifer species (Silver fir, Abies alba; Norway spruce, Picea abies) growing in polluted and unpolluted areas. Two regions in northern Romania (Bicaz and Tarnița) that were subjected to historical pollution changes were selected. Two chemical analyses were used: inductively coupled plasma mass spectrometry (ICP-MS) and X-ray fluorescence spectrometry (XRF). The silver fir trees from the intensively polluted area in the Tarnița region were negatively impacted by industrial pollution: the Mn concentrations were, on average, three times higher in polluted areas than in unpolluted areas (ca. 30 vs. 10 mg kg^-1). This finding was consistent for both ICP-MS and XRF analyses. However, in Norway spruce, this difference was found only in the XRF data, which detected Mn concentrations seven times higher in trees from polluted areas than those from unpolluted areas (ca. 700 vs. 100 mg kg^-1). In the Tarnița region, Norway spruce accumulated more heavy metals than silver fir, but the most pronounced differences between polluted and unpolluted areas were found in silver fir. The two analytical methods are commonly used to determine metal concentrations in wood, and they complement each other, with ICP-MS having a low detection limit for some elements and XRF having higher detection limits and better accuracy. Each method has its advantages and disadvantages, and the optimal method depends on many factors, such as the type of heavy metal analyzed, its concentration in wood, sample type, cost, analysis time, and sample preparation.