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 861.62 million tons and 2315.10 tCO2e, respectively, followed by a consistent decline. (2) Among the three types of solid waste, spent grain exhibited the highest generation rate, contributing to 94.38% of the total. (3) The emergence of China's beer industry dates back to the 1980s in the northeastern region, expanding to the southeastern and the Yangtze River Basin during the 1990s, ultimately extending nationwide. (4) The spatial distribution of beer production revealed significant regional disparities and notable industry concentration. Notably, many provinces witnessed reduced CEs from beer production starting in 2015, although the extent of reduction varied in different provinces. These findings serve as a scientific foundation for formulating emission reduction strategies in beer producing and offer insights for other food industries in China.

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. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.