One of the most significant elements in the environment is arsenic (As). It is a hazardous metalloid that causes contamination of soil and water supplies as a result of numerous anthropogenic and natural sources. This pollution has detrimental impacts on human health and the ecosystem, making it crucial to monitor and control. The release of As from minerals into the soil and groundwater depends on the kind of mineral, pH, and redox conditions. As is typically found in two forms, trivalent arsenic (As³⁺, arsenite) and pentavalent arsenic (As⁵⁺, arsenate), both of which are inorganic and extremely deadly. When it comes to toxicity, arsenite is more dangerous than arsenate. Many people have been affected by As poisoning, due to exposure to As through water and food. Nowadays, accurate, fast measurement of As in the field remains a technical challenge; however, the X-ray fluorescence method is considered the most reliable, cost effective and capable of measuring a wide spectrum of metals. Health risk assessment is a crucial parameter to estimate the health hazards of the As molecule. The risk assessment approach proposed by the USEPA is common and widely used, amongst others. On the numerous health risk assessment models/frameworks used to foresee the carcinogenic and non-carcinogenic health impacts brought on by As-contaminated water, little information is currently accessible. This discussion also emphasizes the need for more study on the risks to human health posed by specific As species (both organic and inorganic) found in As-contaminated water.

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.

Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the factors that influence wildlife–vehicle collisions (WVCs) and provide mitigation methods. Most of these studies examined road density, traffic volume, seasonal fluctuations, etc. However, in analysing the distribution of WVC, few studies have considered a spatial and significant distance geostatistical analysis approach that includes how different land-use categories are associated with the distance to WVCs. Our study investigated the spatial distribution of agricultural land, meadows and pastures, forests, built-up areas, rivers, lakes, and ponds, to highlight the most dangerous sections of roadways where WVCs occur. We examined six potential ‘hot spot’ distances (5–10–25–50–100–200 m) to evaluate the role different landscape elements play in the occurrence of WVC. The near analysis tool showed that a distance of 10–25 m to different landscape elements provided the most sensitive results. Hot spots associated with agricultural land, forests, as well as meadows and pastures, peaked on roadways in close proximity (10 m), while hot spots associated with built-up areas, rivers, lakes, and ponds peaked on roadways farther (200 m) from these land-use types. We found that the order of habitat importance in WVC hot spots was agricultural land < forests < meadows and pastures < built-up areas < rivers < lakes and ponds. This methodological approach includes general hot-spot analysis as well as differentiated distance analysis which helps to better reveal the influence of landscape structure on WVCs.

We investigated the relationship between lunar illumination based on the percentage of the visible lunar disk (LDP) and the frequency of wildlife–vehicle collisions (WVCs) in Lithuania. We analyzed WVC frequency during ten 10% LDP intervals to more precisely reflect the relationship between LDP and WVC. The 10% LDP interval approach showed a significant trend of increasing WVC frequencies with an increasing LDP at night. We also examined the correlation between the daily numbers of WVCs and LDP for different months and seasons. The relationship seemed to be stronger at night and during the late autumn–winter months, particularly in December, suggesting the importance of lunar illumination on WVCs. There was a weak positive correlation between LDP and overall daily number of WVCs (r_s = 0.091; p < 0.001) and between LDP and night WVCs (r_s = 0.104; p < 0.001). We found significant positive correlations for winter (December–February) (r_s = 0.118; p = 0.012) and autumn (August–November) (r_s = 0.127; p = 0.007). Our study suggests that the LDP interval approach may provide more possibilities for the evaluation and quantification of WVCs and lunar light relationships than the traditional lunar phase approach.