Background.China has undergone a rapid industrial revolution and urbanization during the past three decades. This expansion is largely responsible for the release of a large amount of heavy metals into soils and is increasingly raising concerns over the potential effects on human health and the environment. The problem is drawing increasing attention, especially after an extensive nationwide soil survey report in 2014. A number of studies have examined soil contamination by heavy metals in China. However, most of these studies have been small in scale and it is therefore challenging to get a general overview of the level of contamination across the entire country.Objectives.The present study is aimed at presenting a synthesized overview of the extent, pattern, and impact of heavy metal contamination of soils in China, including mitigation approaches.Methods.Eighty-six journal articles and other literature such as reports, internet sources, and statistical yearbooks were narratively and critically synthesized to compile a holistic summary of sources of heavy metals, the extent of pollution, spatial distribution and impact of heavy metal contamination in China. The major findings from these studies are presented, along with mitigation approaches applicable to China.Discussion.A synthesis of major findings from recent scientific journals shows that about 10.18% of farmland soils which supports 13.86% of grain production in China is affected by heavy metals. The main sources of pollution are anthropogenic activities. Even though the spatial distribution of pollution is highly variable owing to natural and human factors, provinces with intensive industrial activities such as Henan, Shandong, and Sichuan are more highly polluted than others. These regions are top grain producing areas and hence require close follow-up for development of feasible approaches to mitigating crop contamination and associated health risks emerging in parts of China. The government recently launched a program aimed at determining sound reclamation strategies.Conclusion.Mitigation of heavy metal contamination in China requires coordination of different actors and integration of all feasible reclamation approaches.Competing Interests.The authors declare no competing financial interests.

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Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions.