Assessment of chronic impact of metallic nanoparticles (NPs) in soil ecosystems is a necessity for ensuring safe and sustainable application. NPs affect plants and their associated microbial life, while the plants and their associated microbiota affect the NPs' fate. Here, we measured the available Ag pool (determined as diethylenetriaminepentaacetic acid-extractable Ag) in AgNP-amended sandy loam soil (1, 10, and 50 mg Ag per kg of soil) over a period of 63 d with and without lettuce. The associated impacts on soil pH, Ag accumulation in lettuce, and the responses of the rhizosphere bacterial community were determined. We found that the addition of AgNPs significantly increased the soil pH from 7.70 to 7.87 after a short-term (7 d) incubation. Noteworthily, the extractability of Ag in AgNP-amended soil was concentration-dependent and changed over time because of their continuous dissolution and uptake by plants. Ag uptake and upward translocation in lettuce positively correlated with the extractable Ag content in soil. Furthermore, a long-term (63 d) exposure to 50 mg/kg of AgNPs altered the structure and composition of the rhizosphere bacterial community potentially by regulation of bacterial groups associated with element (e.g., N and S) cycling and stress tolerance. In conclusion, our results demonstrated that the dynamic dissolution of AgNPs in sandy loam soil plays an important role in influencing the overall Ag bioavailability of the NPs in plants. The enhanced effects of AgNPs on the alterations in the rhizosphere bacterial community highlight that the long time-resolved dynamics of NP exposure should be taken into consideration for accurate ecological risk assessment of NPs in the soil ecosystem.

Five-dimensional (5D) seismic data reconstruction becomes more appealing in recent years because it takes advantage of five physical dimensions of the seismic data and can reconstruct data with large gap. The low-rank approximation approach is one of the most effective methods for reconstructing 5D dataset. However, the main disadvantage of the low-rank approximation method is its low computational efficiency because of many singular value decompositions (SVD) of the block Hankel/Toeplitz matrix in the frequency domain. In this paper, we develop an SVD-free low-rank approximation method for efficient and effective reconstruction and denoising of the seismic data that contain four spatial dimensions. Our SVD-free rank constraint model is based on an alternating minimization strategy, which updates one variable each time while fixing the other two. For each update, we only need to solve a linear least-squares problem with much less expensive QR factorization. The SVD-based and SVD-free low-rank approximation methods in the singular spectrum analysis (SSA) framework are compared in detail, regarding the reconstruction performance and computational cost. The comparison shows that the SVD-free low-rank approximation method can obtain similar reconstruction performance as the SVD-based method but with a large computational speedup.