The meliorization process of urban green spaces: Integrating landsense creation for sustainable development

Urban green spaces play a crucial role in enhancing the well-being of urban residents and promoting sustainable urban development. However, optimizing the planning and management of urban green spaces to meet residents' diverse needs and preferences poses a considerable challenge. This study addresses this challenge by employing a landsenses ecology approach, integrating residents' perspectives into the planning and design of urban green spaces. Starting from human needs, a conceptual framework for the meliorization model of urban green spaces is constructed, grounded in the principles of landsense creation and incorporating a “design-simulation-management” process. Through this model, the mechanisms driving the meliorization process are explored. This study contributes to improving the meliorization process in landsenses ecology, while expanding the theoretical framework and methodology of landscape ecology. By emphasizing the dynamic interactions between land planning, construction, and residents' experiences, this study provides valuable insights into the dynamic development of urban green spaces, facilitating the implementation of sustainable urban development strategies and practices.

Co anchored on porphyrinic triazine-based frameworks with excellent biocompatibility for conversion of CO_(2)in H_(2)-mediated microbial electrosynthesis

Microbial electrosynthesis is a promising alternative to directly convert CO_(2)into long-chain compounds by coupling inorganic electrocatalysis with biosynthetic systems.However,problems arose that the conventional electrocatalysts for hydrogen evolution may produce extensive by-products of reactive oxygen species and cause severe metal leaching,both of which induce strong toxicity toward microorganisms.Moreover,poor stability of electrocatalysts cannot be qualified for long-term operation.These problems may result in poor biocompatibility between electrocatalysts and microorganisms.To solve the bottleneck problem,Co anchored on porphyrinic triazine-based frameworks was synthesized as the electrocatalyst for hydrogen evolution and further coupled with Cupriavidus necator H16.It showed high selectivity for a four-electron pathway of oxygen reduction reaction and low production of reactive oxygen species,owing to the synergistic effect of Co–Nx modulating the charge distribution and adsorption energy of intermediates.Additionally,low metal leaching and excellent stability were observed,which may be attributed to low content of Co and the stabilizing effect of metalloporphyrins.Hence,the electrocatalyst exhibited excellent biocompatibility.Finally,the microbial electrosynthesis system equipped with the electrocatalyst successfully converted CO_(2)to poly-β-hydroxybutyrate.This work drew up a novel strategy for enhancing the biocompatibility of electrocatalysts in microbial electrosynthesis system.

A Deep Learning Application for Building Damage Assessment Using Ultra-High-Resolution Remote Sensing Imagery in Turkey Earthquake

Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses.In February 2023,two magnitude-7.8 earthquakes struck Turkey in quick succession,impacting over 30 major cities across nearly 300 km.A quick and comprehensive understanding of the distribution of building damage is essential for e fficiently deploying rescue forces during critical rescue periods.This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye.Based on high-resolution remote sensing data from WorldView2,BDANet used predisaster imagery to extract building outlines;the image features before and after the disaster were then combined to conduct building damage assessment.We optimized these results to improve the accuracy of building edges and analyzed the damage to each building,and used population distribution information to estimate the population count and urgency of rescue at different disaster levels.The results indicate that the building area in the Islahiye region was 156.92 ha,with an affected area of 26.60 ha.Severely damaged buildings accounted for 15.67%of the total building area in the affected areas.WorldPop population distribution data indicated approximately 253,297,and 1,246 people in the collapsed,severely damaged,and lightly damaged areas,respectively.Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.

Most root-derived carbon inputs do not contribute to long-term global soil carbon storage

Plant root-derived carbon(C)inputs(I_(root))are the primary source of C in mineral bulk soil.However,a fraction of I_(root)may lose quickly(I_(loss),e.g.,via rhizosphere microbial respiration,leaching and fauna feeding)without contributing to long-term bulk soil C storage,yet this loss has never been quantified,particularly on a global scale.In this study we integrated three observational global data sets including soil radiocarbon content,allocation of photo synthetically assimilated C,and root biomass distribution in 2,034 soil profiles to quantify I_(root)and its contribution to the bulk soil C pool.We show that global average I_(root)in the 0-200 cm soil profile is 3.5 Mg ha^(-1)yr^(-1),~80%of which(i.e.,I_(loss))is lost rather than co ntributing to long-term bulk soil C storage.I_(root)decreases exponentially with soil depth,and the top 20 cm soil contains>60%of total I_(root).Actual C input contributing to long-term bulk soil storage(i.e.,I_(root)-I_(loss))shows a similar depth distribution to I_(root).We also map I_(loss)and its depth distribution across the globe.Our results demonstrate the global significance of direct C losses which limit the contribution of I_(root)to bulk soil C storage;and provide spatially explicit data to facilitate reliable soil C predictions via separating direct C losses from total root-derived C inputs.