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Ryegrass root-soil composites mechanical properties and its slope stability: Experimental study and numerical analysis.
J Air Waste Manag Assoc
February 2025
School of Rail Transportation, Soochow University, Suzhou, People's Republic of China.
Urbanization and infrastructure projects generate huge amount of construction and demolition waste (CDW), posing significant challenges for the environment and human health. In order to reduce the environment and safety risks caused by the CDW landfills, this study was amid to utilize plant roots to develop a root-CDW-soil system for strengthening the CDW and enhancing the slope stability of CDW landfills. A series of experimental analyses were conducted, focusing on shear tests of root-soil composites under various moisture conditions and root content ratios.
View Article and Find Full Text PDFFirst Comprehensive List of Phlebotomine Sand Fly Species (Diptera: Psychodidae) in a Leishmaniasis Focus (Djelfa, Algeria), Including the First Record of Females in Algeria.
J Arthropod Borne Dis
June 2024
Exploration and Valorization of Steppe Ecosystems Laboratory, Faculty of Nature and Life science, University of Djelfa, Djelfa, Algeria.
Background: The wide distribution of phlebotomine vectors complicates the leishmaniasis situation in the world, with the risk of spreading from rural to urban areas. Our study investigates for the first time the ecology and distribution of sand fly populations in leishmaniasis focus (Djelfa, Algeria).
Methods: Sampling is performed using light traps from August 2021 to July 2022 at ten sites with different biotopes: two peri-urban stations (Ain Oussera and Hassi Bahbah), one urban station (Djelfa), and three rural stations (Ain El-Bel, Haniet Ouled Salem and Mlaga).
Evaluation of the water quality monitoring program of Mountain Rivers located in a tropical protected atlantic Forest area (PARNASO, Brazil): are the impacts of the highway really assessed?
Integr Environ Assess Manag
January 2025
Programa de Biologia Marinha e Ambientes Costeiros, Universidade Federal Fluminense (PBMAC-UFF), Niterói, Rio de Janeiro, Brazil.
Road activities are recognized sources of pollution that affect the hydrochemistry of nearby water bodies. This study evaluated the Water Quality Monitoring Program in the Soberbo and Iconha rivers in the Guapi-Macacu watershed, which is affected by the BR-116 highway. The Rio-Teresópolis Concessionaire from 2009 to 2016 carried out quarterly sampling.
View Article and Find Full Text PDFNext-gen agriculture: integrating AI and XAI for precision crop yield predictions.
Front Plant Sci
January 2025
Department of Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing (III TDM), Kurnool, Andhrapradesh, India.
Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors.
View Article and Find Full Text PDFA data-driven framework to identify influencing factors for soil heavy metal contaminations using random forest and bivariate local Moran's I: A case study.
J Environ Manage
January 2025
Chinese Academy of Environmental Planning, Beijing, 100041, China. Electronic address:
The efficacy of traceability analysis is often limited by a lack of information on influencing factors for heavy metal (HM) contaminations in soil, such as spatial correlations between HM concentrations and influencing factors. To overcome this limitation, a novel data-driven framework was established to identify influencing factors for soil HM concentrations in an industrialised study area, in Guangdong Province, China, mainly using random forest (RF) and bivariate local Moran's I (BLMI) on the basis of the 577 soil samples and the 18 environmental covariates. The quantitative contributions of the 18 influencing factors for the Cd, As, Pb, and Cr concentrations were determined by the optimised RF.
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