Recent Progress of Remediating Heavy Metal Contaminated Soil Using Layered Double Hydroxides as Super-Stable Mineralizer

Heavy metal contamination in soil, which is harmful to both ecosystem and mankind, has attracted worldwide attention from the academic and industrial communities. However, the most-widely used remediation technologies such as electrochemistry, elution, and phytoremediation. suffer from either secondary pollution, long cycle time or high cost. In contrast, in situ mineralization technology shows great potential due to its universality, durability and economical efficiency. As such, the development of mineralizers with both high efficiency and low-cost is the core of in situmineralization. In 2021, the concept of ‘Super-Stable Mineralization’ was proposed for the first time by Kong et al.[1] The layered double hydroxides (denoted as LDHs), with the unique host–guest intercalated structure and multiple interactions between the host laminate and the guest anions, are considered as an ideal class of materials for super-stable mineralization. In this review, we systematically summarize the application of LDHs in the treatment of heavy metal contaminated soil from the view of: 1) the structure–activity relationship of LDHs in in situ mineralization, 2) the advantages of LDHs in mineralizing heavy metals, 3) the scale-up preparation of LDHs-based mineralizers and 4) the practical application of LDHs in treating contaminated soil. At last, we highlight the challenges and opportunities for the rational design of LDH-based mineralizer in the future

Dense Temperature Mapping and Heat Wave Risk Analysis Based on Multisource Remote Sensing Data

As high temperature and heat wave have become great threats to human survival, social stability, and ecological safety, it is of great significance to master the spatial and temporal dynamic changes of temperature to prevent high temperature and heat wave risks. The meteorological station can provide accurate near-ground temperature, but only within a specific space and time. In order to meet the needs of large-scale research, spatial interpolation methods were widely used to obtain spatially continuous temperature maps. However, these methods often ignore the influence of external factors on temperature, such as land cover, height, etc., and neglect to supplement temporal-wise information. To deal with these issues, a joint spatio-temporal method is proposed to obtain dense temperature mapping from multisource remote sensing data, which combining a geographically weighted regression model and a polynomial fitting model. Besides, a heat wave risk model is also built based on the dense temperature maps and population data, in order to evaluate the heat wave risk of different areas. Accuracy evaluations and experiments have verified the effectiveness of the proposed methods. Case study on the four cities of Zhejiang Province, China have demonstrated that areas with higher degree of urbanization are often accompanied by higher heat wave risks, such as the northern part of the study area. The study also found that the heat wave risks have presented a centralized distribution and spatial autocorrelation characteristics in the study area