Soil contamination by metals and metalloids (metal[loid]s) is a global issue with significant risks to human health, ecosystems, and food security. Accurate risk assessment depends on understanding metal(loid) mobility, which dictates bioavailability and environmental impact. Here we show a theory-guided machine learning model that predicts soil metal(loid) fractionation across the globe. Our model identifies total metal(loid) content and soil organic carbon as primary drivers of metal(loid) mobility. We find that 37% of the world’s land is at medium-to-high mobilization risk, with hotspots in Russia, Chile, Canada, and Namibia. Our analysis indicates that global efforts to enhance soil carbon sequestration may inadvertently increase metal(loid) mobility. Furthermore, in Europe, the divergence between spatial distributions of total and mobile metal(loid)s is uncovered. These findings offer crucial insights into global distributions and drivers of soil metal(loid) mobility, providing a robust tool for prioritizing metal(loid) mobility testing, raising awareness, and informing sustainable soil management practices.

Human behavior is notoriously difficult to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring about long-term behavioral changes. During the pandemic, people have been forced to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. A critical question going forward is how these experiences have actually changed preferences and habits in ways that might persist after the pandemic ends. Many observers have suggested theories about what the future will bring, but concrete evidence has been lacking. We present evidence on how much US adults expect their own postpandemic choices to differ from their prepandemic lifestyles in the areas of telecommuting, restaurant patronage, air travel, online shopping, transit use, car commuting, uptake of walking and biking, and home location. The analysis is based on a nationally representative survey dataset collected between July and October 2020. Key findings include that the “new normal” will feature a doubling of telecommuting, reduced air travel, and improved quality of life for some.

Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO₂ and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO₂ and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO₂ and CO.

Topsoil arsenic (As) contamination threatens the ecological environment and human health. However, traditional methods for As identification rely on on-site sampling and chemical analysis, which are cumbersome, time-consuming, and costly. Here we developed a method combining visible near infrared spectra and deep learning to predict topsoil As content. We showed that the optimum fully connected neural network model had high robustness and generalization (R-Square values of 0.688 and 0.692 on the validation and testing sets). Using the model, the relative As content at regional and global scales were estimated and the human populations that might potentially be affected were determined. We found that China, Brazil, and California are topsoil As-contamination hotspots. Other areas, e.g., Gabon, although also at great risk, are rarely documented, making them potential hotspots. Our results provided guidance for regions that require more detailed detection or timely soil remediation and can assist in alleviating global topsoil-As contamination.