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Following rapid social and economic development over the past several decades, soil pollution by heavy metals (HMs) has been both serious and widespread in China. The Soil Environmental Quality Standards (SEQSs) in China (GB 15618-1995) have been introduced to encourage and enforce sustainable soil HM management. However, in recent years, HM contents in soils have frequently been found to exceed their associated standard values, while the crops growing on them might still meet regulatory standards, and vice versa. There is thus growing awareness that GB 15618-1995 does not effectively regulate current soil HM pollution, as it has encountered bottlenecks, and disappointing outcomes caused by poor execution along with deficiencies and gaps in the policies. However, due to the deficiency of scientific research about relationships between soil HM concentrations and their ecological or human health effects, the development of SEQSs in China is still greatly restricted. This paper discusses international SEQSs of HMs as well their development in China over time, then examines current Chinese SEQSs to demonstrate their potential regulatory deficiencies by referring to international SEQSs. The corresponding legislative policies are described, and scientific information or responses are outlined for maintaining soil environmental quality. China's experience has shown that policy and science can be linked to work in tandem to better understand and manage soil quality issues.

Since sparse representation (SR) was first introduced into robust face recognition, the argument has lasted for several years about whether sparsity can improve robust face recognition or not. Some work argued that the robust sparse representation (RSR) model has a similar recognition rate as non‐sparse solution, while it needs a much higher computational cost due to the larger feature dimensionality in the pixel space. In this study, the authors reveal that the standard RSR model, which expands the dictionary with the identity matrix to reconstruct corruption or occlusion in face images, is essentially a non‐sparse solution with a relatively large residual. The reason why the RSR model underperforms may be its inappropriately expanded bases rather than the sparsity itself. Thereby, this study proposes to design a dictionary with an expanded noise bases set which can precisely reconstructs any corruption or occlusion in face images in a subspace. Experimental results show that the algorithm can greatly improve recognition rates for robust face recognition. In addition, the algorithm can be simply performed in a subspace with a small feature dimensionality, thus efficient enough for real systems. This study makes us come to the conclusion that solving the approximation problem in raw pixel space is not necessary for robust face recognition, while solving in a subspace with a much smaller feature dimensionality is enough when the dictionary is well expanded. Finally, this study also confirms that the sparsity plays an important role in SR based classification.