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The environmental restoration and ecological treatment of abandoned mining areas are of great significance for the construction of ecological civilization. In view of the heavy metal pollution in soil caused by mining, it is particularly important to carry out the investigation of heavy metal pollution characteristics and health risk assessment for the design and construction of mining treatment schemes. The pollution characteristics of soil heavy metals(As, Cd, Cu, Mn and Ni)of Dafanshan abandoned alum mining area in Lujiang county of Anhui province were analyzed, and the pollution assessment and human health risk assessment were established by combining the site accumulation index method, potential ecological risk index method and health risk assessment model(USEPA).The results show that the contents of As and Cd in the soil of the abandoned alum mining area significantly exceed the standard, and compared with the risk screening value of agricultural land, the exceeding rates are 77% and 100%, respectively; Cu in soil is slightly polluted, and the exceeding rate is 16%; the contents of Mn and Ni do not exceed the standard; in addition, the above five heavy metal elements in the descending order of the coefficients of variation are Ni, Mn, Cu, Cd and As; the study area has strong potential ecological risk, and the potential ecological risk index shows that As and Cd are the main risk elements, and the soil is mainly in quite-strong and very-strong potential ecological risks. The health risk assessment model shows that the indirect oral intake of excessive heavy metals in soil is the main route of non-carcinogenic and carcinogenic risks; As and Cd are within the acceptable ranges of non-carcinogenic and carcinogenic risks for adults and children through oral ingestion, and there is no carcinogenic risk for adults and children through respiratory ingestion and skin contact. [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]

[Objectives]This paper aimed to evaluate the validity of pesticides application strategy in rice, and analyse the risk of dietary exposure and ecological impact. [Methods] This study implemented long-term investigation on usage and residual details of pesticides using for pest control in single-season and double-season rice of Jiangsu and Jiangxi provinces. Investigation of pesticides residues in rice and soil had been conducted in the period of tillering, heading and harvesting stage, and the results had been compared between the pesticide reduction strategies adopted area and the regular integrated pest management (IPM) adopted area. Acute and chronic dietary exposed risk and ecological impact risks had been evaluated by using the dietary exposure risk assessment model and the ecology relative risk model (EcoRR). [Results] The results revealed that.4 of total 51 pesticides used in rice for pest control, had been found in the harvest brown rice. Pymetrozine and hexaconazole were the most frequently found pesticides in all rice samples with the frequency up to 93.00% and 28.75%, followed by 14.29% and 7.14% for chlorpyrifos and pentazolidol, respectively. However, the risk assessment of acute and chronic toxicity that may be caused by these 4 pesticides through dietary exposure, was finalized to be negligible for all populations divided by age. Adopting pesticide reduction strategies could further low the risk of dietary exposure. As for the soil sampled from the period of tillering, heading, and harvesting stages of rice, 13 different kinds of pesticides were found, tricy clazole, hexaconazole and propiconazole with 100% detection rates in particular. It could be explained by the long half-life periods of these pesticides and the remains of previous residue. The ecological risk assessment showed that the reduction use of pesticides could decrease the ecological risks significantly, even down to low level from medium level risk. It was found that the most effective effort of reducing the ecological impact was to decrease the use of pymetrozine, as this pesticide played a key role to the contribution of ecological risks in the period of tillering, heading, and harvesting ( 54.16% -83.68%, 19.07%-84.39%, and 0.00%-82.32% respec tively). Secondly, the use of pretilachlor at tillering stage, difenoconazole at the heading stage and propiconazole at the harvest stage was considered to be alternative key elements for the cause of ecological risk, with contributions of the risk ratios of 0.00%-71.50%. 0.00%-68.95% and 1.12%-62.24%,respectively. [Conclusions] Reducing the use of specific pesticides in the particular growth period of rice could reduce ecological impact and dietary exposure significantly. Pymetrozine plays a key role for ecological impact, followed by difenoconazole, propiconazole and pretilachlor, which should be used appropriately. [ABSTRACT FROM AUTHOR]