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This fully rewritten third edition has wider scope and expanded coverage. It offers the various scientists using data on soil contaminants a detailed account of the general principles, followed by focused assessment of 21 elements from antimony to zinc.

With the development of hyperspectral imaging technology, the potential for utilizing hyperspectral images to accurately estimate heavy metal concentrations in regional soil has emerged. Currently, soil heavy metal inversion based on laboratory hyperspectral data has demonstrated a commendable level of accuracy. However, satellite images are susceptible to environmental factors such as atmospheric and soil background, presenting a significant challenge in the accurate estimation of soil heavy metal concentrations. In this study, typical chromium (Cr)-contaminated agricultural land in Shaoguan City, Guangdong Province, China, was taken as the study area. Soil sample collection, Cr content determination, laboratory spectral measurements, and hyperspectral satellite image collection were carried out simultaneously. The Zhuhai-1 hyperspectral satellite image spectra were corrected to match laboratory spectra using the direct standardization (DS) algorithm. Then, the corrected spectra were integrated into an optimal model based on laboratory spectral data and sample Cr content data for regional inversion of soil heavy metal Cr content in agricultural land. The results indicated that the combination of standard normal variate (SNV)+ uninformative variable elimination (UVE)+ support vector regression (SVR) model performed best with laboratory spectral data, achieving a high accuracy with an R2 of 0.97, RMSE of 5.87, MAE of 4.72, and RPD of 4.04. The DS algorithm effectively transformed satellite hyperspectral image data into spectra resembling laboratory measurements, mitigating the impact of environmental factors. Therefore, it can be applied for regional inversion of soil heavy metal content. Overall, the study area exhibited a low-risk level of Cr content in the soil, with the majority of Cr content values falling within the range of 36.21–76.23 mg/kg. Higher concentrations were primarily observed in the southeastern part of the study area. This study can provide useful exploration for the promotion and application of Zhuhai-1 image data in the regional inversion of soil heavy metals.

PurposeThe content of heavy metals in the soil is directly related to the control of soil pollution, but due to the limitations of manpower and material resources, it is difficult to detect them in detail; researchers usually need to predict the content of soil heavy metals in unknown areas based on existing data. Therefore, how to choose an effective method to complete this process has become a challenging problem.Materials and methodsIn this paper, a deep composite model (DCM) is proposed. The model is based on radial basis function neural network (RBFNN), then, uses self-adaptive learning based particle swarm optimization algorithm (SLPSO) to generate the weight and bias of the output layer of RBFNN and employs adaptive adjustment based root mean square back-propagation (ARMSProp) to optimize all variables of RBFNN, so as to improve the prediction accuracy of the model for soil heavy metal content. When using this model to predict soil heavy metal content, the Pearson coefficient is used as a comparison index to compare the correlation between different heavy metals and heavy metals to be predicted, and finally the content of heavy metals with a Pearson coefficient greater than 0.5 is selected as the input of the model variable.Results and discussionFirst in the validation of the proposed SLPSO algorithm, the effectiveness of SLPSO and the feasibility of being applied to the DCM model have been proved. Then, the DCM was applied to the prediction of soil heavy metal content in six new urban areas of Wuhan in China, the experimental results show that the predicted value of soil heavy metal content of DCM is closer to the actual value than other comparison models, and the four error indicator values of DCM are also significantly lower than other comparison models, especially when compared with RBFNN, the MAPE and SMAPE of DCM have dropped by 8.6% and 3.9%, respectively.ConclusionsWe can conclude that the deep composite model proposed in this paper obtains a good prediction accuracy when predicting soil heavy metal content; it has certain feasibility and can be used as an effective method for soil heavy metal content prediction.

Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling R2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R2 is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.

This title includes a number of Open Access chapters.Although adverse health effects of heavy metals have been known for a long time, exposure to heavy metals continues and is even increasing in some areas. Remediating heavy metal contaminated soils and water is necessary to reduce the associated health and ecological risks, make the land resource available for agricultural production, enhance food security, and scale down land tenure problems. This book discusses the causes and the environmental impact of heavy metal contamination. It then explores many exciting new methods of analysis and decontamination currently studied and applied in the field today.

Urban soils are a mixture of natural soil-forming factors and anthropogenic activities (Shuster and Dadio, 2018). [...]they require an adapted set of indicators for a soil quality assessment. Furthermore, sample plots were assigned one of three garden habitat types: vegetable beds (i.e., annual vegetable plants), flower beds and berry cultivations (i.e., perennial flowers, roses, and berry shrubs), and lawn (i.e., meadows and turf). [...]our study may help to analyze the effect of garden management or urbanization on soil quality (see Tresch et al., 2018) or provide data for modeling of carbon dynamics in urban soils or other soil based ecosystem services. Simon Tresch1,2,3*, Marco Moretti3, Renée-Claire Le Bayon1, Paul Mäder2, Andrea Zanetta3,4, David Frey3,5, Bernhard Stehle2,6, Anton Kuhn2, Adolphe Munyangabe2 and Andreas Fliessbach2 * 1Functional Ecology Laboratory, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland * 2Department of Soil Sciences, Research Institute of Organic Agriculture (FiBL), Frick, Switzerland * 3Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland * 4Department of Environmental System Science, Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland * 5Department of Biology, University of Fribourg, Fribourg, Switzerland * 6Department of Biology, Ecology, University of Konstanz, Konstanz, Germany

A comparative assessment of the ecological and geochemical condition of soils of the city of Ul’yanovsk and its park territories is given. Some physical and chemical indicators of the soil environment (pH, exchange base, humus, sulfates, chlorides, etc.) are determined. Schematic maps of heavy-metal content in soils of the city are made. The main problems of the ecological condition of parks under conditions of urbanization are revealed.