Background: Soil contamination resulting from the use and handling of petrochemicals and other petroleum products during power generation activities is an increasing global concern due to its adverse impact on the ecosystem.
Objectives: The present study was carried out to determine the concentrations and speciation of potentially toxic metals in oil-contaminated soils around transformer installation areas in Ile-Ife, Nigeria, and to confirm soil pollution levels with hazard quotient and hazard index analysis.
Methods: Soils from the transformer oil-contaminated and uncontaminated (control) areas were collected at 0-15 cm and 15-30 cm depths and analyzed for heavy metal concentrations using atomic absorption spectrometry. The metals were fractionated and their hazard evaluated to confirm the pollution level of the contaminated soils.
Results: The concentrations of cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in the two sets of oil-impacted soils were higher than in the control soils. The metals also had relatively moderate bioavailability and mobility potential with more of the proportion retained in the residual fraction. Chronic daily intake (CDI) of the metals increased in the order of: Cd < Cr < Pb < Ni < Mn < Cu < Zn < Fe, while chronic daily intake risk exposure pathway followed the order of: CDI < CDI < CDI
Conclusions: The study concluded that the concentrations of the metals were within permissible limits, but the chronic daily dosage was significant and may pose a health hazard to humans with long term exposure to these heavy metal contaminants.
Competing Interests: The authors declare no competing financial interests.
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http://dx.doi.org/10.5696/2156-9614-9.24.191213 | DOI Listing |
Sci Rep
December 2024
Department of Electrical Engineering, College of Engineering, Taif University, P.O. BOX 11099, 21944, Taif, Saudi Arabia.
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy.
View Article and Find Full Text PDFSci Total Environ
January 2025
College of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China. Electronic address:
To address the challenge of runoff prediction in cold alpine regions with complex spatial distributions, this study proposes an integrated "Water-Soil-Hseat" framework for runoff modeling. This framework incorporates key factors such as precipitation, glacier meltwater, soil spatial distribution, and temperature-induced melt processes, providing a more comprehensive understanding of runoff generation mechanisms. Precipitation and glacier meltwater serve as the primary hydrological variables, while soil spatial distribution acts as an impact factor, and temperature-induced melt processes drive the runoff.
View Article and Find Full Text PDFSci Rep
October 2024
Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python's Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values.
View Article and Find Full Text PDFHeliyon
September 2024
Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
This study leverages the BERTopic algorithm to analyze the evolution of research within precision agriculture, identifying 37 distinct topics categorized into eight subfields: Data Analysis, IoT, UAVs, Soil and Water Management, Crop and Pest Management, Livestock, Sustainable Agriculture, and Technology Innovation. By employing BERTopic, based on a transformer architecture, this research enhances topic refinement and diversity, distinguishing it from traditional reviews. The findings highlight a significant shift towards IoT innovations, such as security and privacy, reflecting the integration of smart technologies with traditional agricultural practices.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
September 2024
Division of Agricultural Chemicals, ICAR-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, 110012, India.
The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network (BN), quantitative structure-activity relationship-density functional theory (QSAR-DFT), artificial neural network (ANN), molecular docking (MD), and molecular dynamics stimulation (MS) of PCB biodegradation, i.e.
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