Our paper reviews the current understanding of mercury in the environment of soil and sediment, including sampling, mobilization phases and analyzing methods. As a dangerous trace element, mercury has been shown to have several harmful effects on the environment. Mercury is released into the environment in a variety of chemical forms by both geogenic and human activities, with the majority of it coming from anthropogenic sources. It is affected by environmental conditions such as pH, redox potential, light and temperature-all of which determine its final chemical form-reactivity and toxicity. Methylmercury is considered one of the most poisonous forms found in nature. Considering the methodologies of the studies carried out we have found that the best technique for preserving methylmercury in soil and sediment samples is to freeze it immediately after collection. Organically rich soils are related to higher total mercury levels. Plants, such as Solanum nigrum (BR3) and Cynodon dactylon (BR2), can play an important role in mercury transport and accumulation. Solid-phase selenium causes faster demethylation and slower methylation of mercury. Methylmercury can increase by climate change and thawing; arctic permafrost is a potential source of Hg. Chemical vapor generation inductively coupled plasma mass spectrometry was used to develop a simple and quick method for measuring methylmercury; ultrasonic agitation and HNO₃ were used for the process, the last of which proved to be the most efficient for selective extraction of methylmercury.

The main objective of this study is to thoroughly evaluate the diversity and sources of heavy metals in the school environment. Specifically, this study examines the presence of heavy metals in the dust found and collected from 24 schools in Vilnius. Employing hierarchical cluster analysis, principal component analysis, and positive matrix factorization, we identified combustion-related activities as primary contributors to elevated metal concentrations, notably zinc, scandium, and copper, with PM2.5/PM10 ratios indicating a combustion source. They reveal significant differences in the levels of elements such as arsenic (4.55–69.96 mg/kg), copper (51.28–395.37 mg/kg), zinc, and lead, which are affected by both local environmental factors and human activities. Elevated pollution levels were found in certain school environments, indicating environmental degradation. Pollution assessment and specific element pairings’ strong positive correlations suggested shared origins or deposition processes. While this study primarily assesses non-carcinogenic risks to children based on a health risk assessment model, it acknowledges the well-documented carcinogenic potential of substances such as lead and arsenic. The research emphasizes the immediate necessity for efficient pollution management in educational environments, as indicated by the elevated hazard index for substances such as lead and arsenic, which present non-carcinogenic risks to children. This research offers important insights into the composition and origins of dust pollution in schools. It also promotes the need for broader geographic sampling and prolonged data collection to improve our understanding of pollution sources, alongside advocating for actionable strategies such as environmental management and policy reforms to effectively reduce exposure risks in educational settings. Furthermore, it aims to develop specific strategies to safeguard the health of students in Vilnius and similar urban areas.

This study conducts a comprehensive analysis of the presence of heavy metals, specifically zinc (Zn), lead (Pb), copper (Cu), chromium (Cr), and arsenic (As), in dust samples collected from 24 schools in Vilnius during the year 2022. It compares these findings with topsoil data from prior investigations spanning from 2011 to 2023, obtained from the areas near the schools as well as multiple spots across Vilnius. The study reveals significant variations in the levels of heavy metals, providing a comprehensive understanding of the complex relationship between urban sources of pollution, environmental processes, and the correlation between soil and indoor dust pollution. An important aspect of this work is the application of principal component analysis (PCA) and hierarchical clustering on the datasets from 2017 and 2020, which unveiled separate clusters from both dust and soil samples. Three major clusters were identified, highlighting the dynamic character of heavy metal distribution in these environments. Pearson’s correlation analysis provided additional evidence, demonstrating significant relationships between specific heavy metals in both dust and soil samples, emphasizing the interlinked nature of these environments. Zinc (Zn) and Lead (Pb) were determined to be the most commonly found heavy metals in the dust samples, which could potentially pose a health hazard in educational environments. This study distinguishes itself by examining indoor dust in educational facilities and topsoil in Vilnius, providing crucial insights into the relationship between these two environmental matrices. Recognizing the geographical limitations of this study, further research could be expanded to other cities to validate and compare these findings.

Mercury and methylmercury in Baltic Sea sediments, and Polish and Lithuanian soils

This study conducts a comprehensive analysis of the presence of heavy metals, including zinc (Zn), lead (Pb), copper (Cu), chromium (Cr), and arsenic (As), in the dust found inside educational buildings and the top layer of soil in different areas of Vilnius. The study focuses on the analysis of topsoil samples gathered between 2011 and 2023, with a specific emphasis on dust samples obtained from 24 schools in 2022. The research reveals significant variations in the concentrations of heavy metals, providing a clear understanding of the complex relationship between sources of urban pollution, environmental processes, and the correlation between soil and dust contamination by heavy metals. Indoor dust in schools shows unique contamination patterns in contrast to topsoil, suggesting distinctive sources of indoor pollution, while there are some general similarities. This finding emphasizes similarities and distinctions between soil and dust in educational environments. The study utilizes advanced statistical methods such as Pearson correlation, Principal Component Analysis (PCA), and hierarchical clustering to examine the distribution and characteristics of these metals. This study distinguishes itself by examining indoor dust in educational facilities and topsoil in Vilnius, providing crucial insights into the relationship between these two environmental matrices.