Superfund sites are where soil, air, and water are polluted with hazardous materials. Individuals residing and working in these areas are often exposed to metals and other hazardous materials, leading to many adverse health outcomes, including cancer. While individuals are often exposed to multiple chemicals simultaneously, the combined effect of such exposures remains largely unexplored. Here, we investigated the toxicity of metal mixtures in five categories of in vitro assays measuring cytotoxicity, oxidative stress, genotoxicity, cytokine release, and angiogenesis. After testing these mixtures in primary cells and cell lines, we discovered that the nickel/arsenic/cadmium and beryllium/arsenic/cadmium combinations exhibited higher cytotoxicity than their individual compounds, suggesting that the mixtures amplified the cytotoxic effect. To investigate the mechanism underlying their toxicity, we evaluated metal-induced oxidative stress, as oxidative stress is a common factor in most metal-related toxicities. Our results showed that cadmium-induced oxidative stress was increased in mixtures. Some mixtures that induced oxidative stress further increased DNA damage, inhibited DNA synthesis, and activated p53. In addition, some mixtures significantly increased interleukin-8 secretion and angiogenesis more than their component compounds. Our findings offer important insights into metal-related toxicity at Superfund sites.
Increases in the frequency and intensity of droughts and heat waves are threatening forests around the world. Climate-driven tree dieback and mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially finegrained understanding of the site characteristics making forests more susceptible to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate meteorological indicators. To address this research gap, we investigated the drivers of drought-induced forest damage in a particularly drought-affected region of Central Europe using SHapley Additive exPlanations (SHAP) values, an explainable artificial intelligence (XAI) method which allows for the relevance of predictors to be quantified spatially. To develop a reproducible approach that facilitates transferability to other regions, open-source data was used to characterize the meteorological, vegetation, topographical, and soil drivers of tree vulnerability, representing 41 predictors in total. The forest drought response was characterized as a binary variable (damaged or unchanged) at a 30-m resolution based on the Normalized Difference Moisture Index (NDMI) anomaly (%) between a baseline period (2013-2017) and recent years (2018-2022). We revealed critical tipping points beyond which the forest ecosystem shifted towards a damaged state: <81 % tree cover density, <4% of broadleaf trees, and < 24 m canopy height. Our study provides an enhanced understanding of trees' response to drought, which can support forest managers aiming to make forests more climate-resilient, and serves as a prototype for interpretable early-warning systems.
Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, na & iuml;ve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed.