Soil cores and rainwater were sampled under canopies of Cryptomeria japonica in four montane areas along an atmospheric depositional gradient in Kanto, Japan. Soil cores (30cm in depth) were divided into 2-cm or 4-cm segments for analysis. Vertical distributions of elemental enrichment ratios in soils were calculated as follows: (X/Al)(i)/(X/Al)(BG) (where the numerator and denominator are concentration ratios of element-X and Al in the i- and bottom segments of soil cores, respectively). The upper 14-cm soil layer showed higher levels of Cu, Zn, As, Sb, and Pb than the lower (14-30cm) soil layer. In the four areas, the average enrichment ratios in the upper 6-cm soil layer were as follows: Pb (4.93)>or=Sb (4.06)>or=As (3.04)>Zn (1.71)>or=Cu (1.56). Exogenous elements (kg/ha) accumulated in the upper 14-cm soil layer were as follows: Zn (26.0)>Pb (12.4)>Cu (4.48)>or=As (3.43)>or=Sb (0.49). These rank orders were consistent with those of elements in anthropogenic aerosols and polluted (roadside) air, respectively, indicating that air pollutants probably caused enrichment of these elements in the soil surface layer. Approximately half of the total concentrations of As, Sb, and Pb in the upper 14-cm soil layer were derived from exogenous (anthropogenic) sources. Sb showed the highest enrichment factor in anthropogenic aerosols, and shows similar deposition behavior to NO(3)(-), which is a typical acidic air pollutant. There was a strong correlation between Sb and NO(3)(-) concentrations in rainfall (e.g., in the throughfall under C. japonica: [NO(3)(-)]=21.1 [dissolved Sb], r=0.938, p<0.0001, n=182). Using this correlation, total (cumulative) inputs of NO(3)(-) were estimated from the accumulated amounts of exogenous Sb in soils, i.e., 16.7t/ha at Mt. Kinsyo (most polluted), 8.6t/ha at Mt. Tsukuba (moderately polluted), and 5.8t/ha at the Taga mountain system (least polluted). There are no visible ecological effects of these accumulated elements in the Kanto region at present. However, the concentrations of some elements are within a harmful range, according to the Ecological Soil Screening Levels determined by the U.S. Environmental Protection Agency.
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http://dx.doi.org/10.1016/j.scitotenv.2010.01.016 | DOI Listing |
Biodegradation
December 2024
Department of Civil engineering, Islamic Azad university, Mashhad Branch, Iran.
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).
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December 2024
Geotechnical Institute, TU Bergakademie Freiberg, Freiberg, Germany.
The development of new urban areas necessitates building on increasingly scarce land, often overlaid on weak soil layers. Furthermore, climate change has exacerbated the extent of global arid lands, making it imperative to find sustainable soil stabilization and erosion mitigation methods. Thus, scientists have strived to find a plant-based biopolymer that favors several agricultural waste sources and provides high strength and durability for sustainable soil stabilization.
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December 2024
Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran.
This paper presents a ground motion prediction (GMP) model using an artificial neural network (ANN) for shallow earthquakes, aimed at improving earthquake hazard safety evaluation. The proposed model leverages essential input variables such as moment magnitude, fault type, epicentral distance, and soil type, with the output variable being peak ground acceleration (PGA) at 5% damping. To develop this model, 885 data pairs were obtained from the Pacific Engineering Research Center, providing a robust dataset for training and validation.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
College of Chemistry and Materials Science, Jinan University, Guangzhou 510632, China.
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View Article and Find Full Text PDFYing Yong Sheng Tai Xue Bao
October 2024
College of Forestry, Agricultural University of Hebei, Baoding 071000, Hebei, China.
We elucidated the changes of soil microbial biomass and community structure in soil profiles under four typical land use types (farmland, grassland, secondary forest and plantation)and across five soil layers (0-10, 10-20, 20-30, 30-40, 40-50 cm) in the northern mountainous region of Hebei Province. We measured soil microbial biomass by phospholipid fatty acid (PLFA) method, and investigated the effects of land use and soil depth on soil microbial biomass and community structure by variance analysis, correlation analysis and redundancy analysis. The results showed that soil water content, bulk density, and organic carbon content of farmland differed significantly from other land use types.
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