Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils.

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran's index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population.

Characteristics analysis of solid waste generation and carbon emission of beer production in China.

As the largest beer producer and consumer in the world, China's endeavors to reduce solid waste generation (SWG) and carbon emissions (CEs) in the course of beer production assume paramount significance. This study aims to assess the SWG and CEs in beer production within China at both national and provincial levels, and further delves into the spatial distribution characteristics and evolving patterns across the country. Key findings of the study include:(1) Peak SWG and CEs were recorded in 2013, reaching 861.