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Soil pollution is becoming increasingly serious due to resource exploitation in mining areas, which directly affects the stability of the surrounding soil and water environment, and the accurate prediction of heavy metal pollution source analysis has an important guiding role in scientific mine remediation and treatment. On the basis of the previous study, in order to improve the interpretation of the soil media model, positive matrix factorization (PMF) and absolute factor analysis-multiple linear regression (APCS-MLR) models, which have a high degree of maturity and accuracy in the evaluation of heavy metal source analysis in soils, were further selected to enrich the UNMIX model in the previous study. The results of the multiple receptor models analyzing the sources of heavy metal contamination and the contribution of the sources were combined with the ecological risk assessment method to determine the most suitable model in study area under quantitative conditions. The results showed that: 1) the ecological high-risk areas were mainly concentrated in the south and east part of the study area, and Cd was the main risk element of the mine; the mean value of igeoaccumulation index was 3.75, which was highly consistant with the mean value of 731.22 for the potential ecological risk index though latter provided a more comprehensive evaluation of ecological hazards. 2) Comparing the results of the three models, the PMF model resolved four pollution sources: coal combustion source, natural-traffic integrated source, natural source and atmospheric deposition source, and the source contribution rates are 38.15%, 20.62%, 24.28% and 16.95%, respectively. 3) The fitted variables R2 in the PMF model reached to 0.96, which showed the best fitting effect. The PMF model refines source apportionment by accounting for sampling point errors and accurately ascertaining pollution sources and their contributions, making it highly suitable for complex mine-contaminated soils in realistic research settings. The findings of this study can provide a reference for tracing the origins of contaminants, which is essential for the remediation efforts of polluted soils following mining activities in the area. [ABSTRACT FROM AUTHOR]

Bacillus subtilis has great effects on soil water and salt movement. In this paper, 5 different contents of Bacillus subtilis (0, 1, 3, 5 and 7 g/kg) were set, aiming to study the soil water cumulative infiltration, infiltration rate, wetting front, soil water contents and salt contents in each profile, as well as water-stable aggregate contents. The results showed that with the applying of Bacillus subtilis, the cumulative infiltration and infiltration rate in saline-alkali soil decreased obviously, compared to that in the control of soil columns, due to the water retention properties of Bacillus subtilis, especially after the initial 300 min. Moreover, with the increase of Bacillus subtilis content, the cumulative infiltration and infiltration rate first decreased and then increased. When the Bacillus subtilis content was 3 g/kg, the cumulative infiltration and infiltration rate of soil water tended to be the smallest in a certain time, and the variations between the treatments were significant (P<0.01). At the end of the infiltration, the cumulative infiltration with the Bacillus subtilis content of 1, 3, 5 and 7 g/kg was decreased by 18.49%, 21.85%, 12.18% and 3.78% respectively compared with that without Bacillus subtilis. With the applying of Bacillus subtilis, the wetting front decreased as well. At the initial time, there were no significant differences between different treatments. With the infiltration time increasing, the effect of Bacillus subtilis on wetting front in each treatment was significant (P<0.01). The wetting front took 3 700, 7 260, 7 700, 5 100 and 4 320 min to reached the bottom of the soil column with Bacillus subtilis content of 0, 1, 3, 5 and 7 g/kg, respectively, and the migration rates were 7.84×10-3, 4.00×10-3, 3.77×10-3, 5.69×10-3, and 6.71×10-3 cm/min, accordingly. When the content was 3 g/kg, the migration rate of wetting front was the smallest. Both Philip and Green-Ampt model could fit soil infiltration process well with different Bacillus subtilis contents. For Green-Ampt infiltration model, with the increase of Bacillus subtilis contents, soil water saturated hydraulic conductivity first decreased and then increased, and the wetting front suction showed an opposite trend compared with control treatment (P<0.05). For the Philip infiltration model, with the increase of Bacillus subtilis contents, the soil sorptivity and stable infiltration rate first decreased and then increased, which were obviously less than that without Bacillus subtilis (P<0.05). When the Bacillus subtilis was 3 g/kg, the minimum values of soil sorptivity, soil water saturated hydraulic conductivity and stable infiltration rate were obtained, and the maximum value of wetting front suction was obtained, which further confirmed that Bacillus subtilis could increase soil water holding capacity. When the Bacillus subtilis contents were 1, 3, 5 and 7 g/kg, the water contents were greater than that in control. At the depth of 27 cm, the water content in the treatments with Bacillus subtilis of 1, 3, 5 and 7 g/kg increased by 17.65%, 31.76%, 11.76% and 7.06% respectively, compared with that in control. Soil salt contents were also significantly decreased with the application of Bacillus subtilis. The salt content in the treatments with Bacillus subtilis of 1, 3, 5 and 7 g/kg decreased by 22.37%, 31.29%, 17.78% and 10.67% respectively, compared to that in control. After applying Bacillus subtilis, soil water-stable aggregate also increased. In the whole soil layer, the water-stable aggregates in the treatment with Bacillus subtilis of 1, 3, 5 and 7 g/kg were greater than those in control, and increased by 13.02%, 17.59%, 9.68% and 5.24%, respectively. This study can provide a theoretical support for the microorganism application in saline-alkali soil amendment. [ABSTRACT FROM AUTHOR]

High weathered soils in arid and semi-arid area are characterized by low soil fertility and high erosion potential. This paper evaluated the influences of nano-carbon on soil erosion and water loss on the sloping land (15°) of the Chinese Loess Plateau. Simulated rainfall experiments were performed on a natural, fallow loessial slope in the Shenmu Erosion and Environment Research Station, Institute of Soil and Water Conservation, Chinese Academy of Sciences, which locates in the Liudaogou watershed, Shenmu County, Shaanxi Province. Five application rates (i.e., 0, 0.1%, 0.5%, 0.7%, and 1.0%) of nano-carbon were selected during the simulated rainfall experiments (rainfall intensity of 60 mm/h); and 4 vegetation species (i.e., caragana, alfafa, bean, and maize) were planted on the slope to evaluate the vegetation effect on soil and water conservation with the bare as the control. A constant-intensity rainfall method was adopted under natural conditions. Field plots were established on the slope that had been fallow for 5 years. The rainfall simulator came from the independent design of Xi'an University of Technology. Under the 60 mm/h rainfall intensity, the simulated rainfall experiments were carried out for 50 times in the 25 different pretreated plots in order to estimate soil erosion and water loss for all the treatments. The main results were as follows: the initial runoff time was delayed with the increase of nano-carbon contents. Compared to the control treatment without nano-carbon application in bare land, the initial runoff time in alfalfa plot was delayed by 287.1%, which was the most obvious. Furthermore, with the increase of nano-carbon contents, the runoff in each treatment decreased obviously. The nano-carbon alfalfa coverage also had the most obvious effect in the reducing runoff, and the runoff was reduced by 66.47%; for other vegetation species, the reduction was between 31.5% and 33.6%. Moreover, the nano-carbon had a significant effect on soil loss controlling. The nano-carbon in alfalfa land showed the best effect for soil loss controlling, which was followed by caragana. Soybean land showed the highest soil loss. For the bare land (control) and alfalfa coverage, when the nano-carbon content was 1%, the lowest sediment yield was obtained; while for caragana, soybean and maize coverage, when the nano-carbon content was 0.5%, the sediment yield was the lowest. Based on the correlation analysis between nano-carbon and runoff and sediment yield, it could be concluded that nano-carbon has a significant impact on the experimental results. In the evaluation of soil and water loss control effect, the 0.5% nano-carbon had the obvious effect, which was similar with the 0.7% and 1.0% nano-carbon content, so considering the price, 0.5% nano-carbon content was appropriate on soil and water loss control in loess area. The results provide a guide for controlling soil water and nutrient loss on the sloping land. [ABSTRACT FROM AUTHOR]

Aerobic composting is one of the most typical biological processes to decompose organic wastes into organic fertilizers or soil amendments. However, the existing composting has commonly suffered from severe nitrogen loss and long composting periods. Therefore, the additives are required to reduce the composting period and nitrogen loss. Taking the mixture of cow dung and straw as the research material, this study aims to explore the nitrogen loss during rapid composting. The experiment utilized magnetized water (T1), magnetized water combined with biochar (T2), magnetized water combined with ferrous sulfate (T3), and magnetized water combined with Bacillus megaterium (T4), with a control group designated as CK, for a 25-day aerobic composting. The results showed that the high-temperature periods for CK, T1, T2, T3, and T4 were 3- 5, 2-8, 1-8, 4-8, and 2-8 d, respectively. All T1, T2, T3, and T4 were extended the high-temperature period of composting, compared with the CK. The temperature in the T1 treatment coincided with the ambient temperature from day 14 to day 25 of composting, without the secondary fermentation, thereby shortening the composting period. T1, T2, and T4 increased the highest temperature during composting. The most significant effect of T3 was achieved at the maximum temperature of 64.5℃. The total nitrogen content of each treatment group at the end of composting increased by 14.04%, 18.89%, 37.56%, 36.27%, and 28.47%, respectively, compared with the initial total nitrogen content of composting. The total nitrogen content increased the most in the T3 treatment, compared with the CK. The total ammonia emissions in each treatment group were 8.12, 4.37, 6.60, 7.32, and 3.95 g/d, respectively, during composting. The T4 treatment emitted the least ammonia, while the CK was the most. The T3 treatment reduced the pH of the compost during the whole process, whereas the low pH reduced the NH3 emissions. The T4 treatment promoted the nitrate nitrogen content and GI value, with the GI value reaching 110.03% at the end of composting. As such, nitrogen functional genes were then determined to explore the effects of different microbial communities on nitrogen and nitrogen transformation during microbiological composting. The T2 treatment promoted the absolute abundance of the nitrogen cycling gene gdhA, and then suppressed the relative abundance of Actinobacteria, for the high nitrogen cycling. The T3 treatment promoted the absolute abundance of the nitrogen-fixing gene nifH and the nitrogencycling gene gdhA, indicating the relative abundance of Proteobacteria. Additionally, structural equations showed that T2 treatment shared a significant negative effect on ammonia emissions, while ammonia emissions had a significant positive effect on total nitrogen loss. T3 treatment had a significant negative effect on pH, and then pH had a significant positive effect on ammonia emissions, whereas, the ammonia emissions had a significant negative effect on total nitrogen loss. Therefore, the biochar was used to adsorb ammonia for less ammonia emissions, thereby reducing the nitrogen loss. Ferrous sulfate was used to lower the pH of the compost, thus reducing ammonia emissions and nitrogen loss. Magnetized water combined with biochar showed the best nitrogen retention and ideal degree of maturation in the rapid composting, in terms of composting temperature, total nitrogen content, absolute abundance of nitrogen functional genes, relative abundance of microorganisms, and structural equation [ABSTRACT FROM AUTHOR]

Rapidly inverting soil salinity is crucial to the soil water and salt migration for the prevention of the secondary salinization. However, the accuracy and efficiency of soil salinity inversion models are hindered to the coupling relationship between water and salt in soil, particularly for time-consuming and labor-intensive soil collection. This study aims to reduce the interference of soil moisture for obtaining better sample diversity and further improving the robustness of soil salinitu inversion models by using spectral technology. A total of 113 normal and 115 saline soil samples were collected in the Xinjiang cotton fields. These samples were further subjected to different levels of wetting treatment. and subsequently 467 soil samples with varing salt and moisture contents were obtained. Soil salt content was calibrated using the conductivity of soil leaching solution. Spectral data of samples was captured using an ASD ground object spectrometer (400-1000 nm) and a near-infrared spectrometer (960-1693 nm). The soil moisture was also corrected using the external parameter orthogonalization (EPO). Additionally, deep convolutional generative adversarial networks (DCGAN) with different transposed convolution stride strategies were designed to evaluate the sample set using Fréchet Inception Distance (FID) scores. Machine learning models were employed to invert the soil salinity, including partial least squares regression (PLSR), random forest (RF), and onedimensional convolutional neural network (1D-CNN) models using VGG (VNet), EfficientNet (ENet), and ResNet (RNet) architectures. The results demonstrated that the EPO can effectively reduce the interference of moisture on the salinity, indicating the better prediction performance of different models. RNet out performed PLSR, RF, VNet, Enet, RNet, and exhibited the best performance to predict the soil salinity in cotton fields. The lightweight residual neural network without attention mechanism was more suitable for one-dimensional hyperspectral data. There was an increase in the convolution stride and kernel length of the deep convolutional adversarial generative network. The better samples were obtained for the hyperspectral data with long sequences. The superior FID scores were achieved in the generated augmented sample set using Generator B (designed with the larger convolution stride and kernel size), compared with the rest. Specifically, the FID scores were reduced by 7.9% and 13.4%, respectively, compared with GA and GC. The weight distribution of attention was optimized after expanding the training set by DCGAN. The stability and accuracy of the model were further enhanced to predict the soil salinity under certain constraints on training samples. The EPO-DCGAN(GB)-RNet (called EPO-DCGAN-RNet) model was achieved in the superior RMSE and R² values of 136.472 μS/cm and 0.910, respectively, on the validation set, compared with the EPO-SG-RNet (using SG filtering denoising) and EPO-RNet (without sample augmentation). Furthermore, 1D-CNN with Grad CAM was employed to identify the characteristic bands of soil conductivity in the soil leaching solution of cotton field. In summary, an accurate inversion model EPO-DCGAN-RNet was constructed for inversing soil salinity in the cotton fields using spectral technology. Water correction and sample augmentation were incorporated for the soil salt composition. The improved model has the promising potential to the salt-tolerant cotton varieties and irrigation strategies using slightly salty water in cotton fields [ABSTRACT FROM AUTHOR]