Estimating solid waste generation and suitability analysis of landfill sites using regression, geospatial, and remote sensing techniques in Rangpur, Bangladesh

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.

Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh

Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.