Adsorption and Desorption Characteristics of Cd2+ and Pb2+ by Micro and Nano-sized Biogenic CaCO3

The purpose of this study was to elucidate the characteristics and mechanisms of adsorption and desorption for heavy metals by micro and nano-sized biogenic CaCO3 induced by Bacillus subtilis, and the pH effect on adsorption was investigated. The results showed that the adsorption characteristics of Cd2+ and Pb2+ are well described by the Langmuir adsorption isothermal equation, and the maximum adsorption amounts for Cd2+ and Pb2+ were 94.340 and 416.667 mg/g, respectively. The maximum removal efficiencies were 97% for Cd2+, 100% for Pb2+, and the desorption rate was smaller than 3%. Further experiments revealed that the biogenic CaCO3 could maintain its high adsorption capability for heavy metals within wide pH ranges (3–8). The FTIR and XRD results showed that, after the biogenic CaCO3 adsorbed Cd2+ or Pb2+, it did not produce a new phase, which indicated that biogenic CaCO3 and heavy metal ions were governed by a physical adsorption process, and the high adsorptive capacity of biogenic CaCO3 for Cd2+ and Pb2+ were mainly attributed to its large total specific surface area. The findings could improve the state of knowledge about biogenic CaCO3 formation in the environment and its potential roles in the biogeochemical cycles of heavy metals

The Potential Correlation Between Bacterial Sporulation and the Characteristic Flavor of Chinese Maotai Liquor

The relationship between the formation of characteristic Maotai-flavor substances (MTFS) and the dominant bacteria in Maotai Daqu (MTDQ) has long been a topic of research interest in the field of liquor brewing in China. To investigate the connection between MTFS and the Bacillus subtilis (one of dominant bacteria in MTDQ) cultured on solid plates of wheat extract medium at, temperatures of 37, 46, and up to 55°C (Group A), and at a constant 37°C (Group B), the transcriptomes of the bacteria grown in the two groups were studied. About 10 out of 84 differentially expressed genes (DEGs) were related to promoting sporulation. Furthermore, observations made with transmission electron microscopy (TEM) showed that a thicker spore cortex appeared in Group A. The content of 2, 6-pyridinedicarboxylic acid (DPA), an important component of the spore, was 49.77 (±2.50) and 38.23 (±3.96) μg/mg of dried spores from the bacteria cultured in Groups A and B, respectively. Combined with the production process of Maotai liquor, more DPA accumulates in the high-temperature fermentation stage and is then released by spore germination during the subsequent temperature-drop stage. We suggest that DPA (or its derivatives) can then be transformed into MTFS by the Maillard reaction after many rounds of microbial fermentation. The viewpoint that there is a potential correlation between bacterial sporulation and the production of MTFS is proposed

Effects of Moss-Dominated Biocrusts on Soil Microbial Community Structure in an Ionic Rare Earth Tailings Area of Southern China

Moss-dominated biocrusts are widespread in degraded mining ecosystems and play an important role in soil development and ecosystem primary succession. In this work, the soil microbial community structure under moss-dominated biocrusts in ionic rare earth tailings was investigated to reveal the relationship between different types of moss and taxonomy/function of microbiomes. The results showed that microbial community structure was significantly influenced by four moss species (Claopodium rugulosifolium, Orthotrichum courtoisii, Polytrichum formosum, and Taxiphyllum giraldii). The microbial assembly was more prominent in Claopodium rugulosifolium soil than in the other moss soils, which covers 482 bacterial genera (including 130 specific genera) and 338 fungal genera (including 72 specific genera), and the specific genus is 40% to 1300% higher than that of the other three mosses. Although only 141 and 140 operational taxonomic units (OTUs) rooted in bacterial and fungal clusters, respectively, were shared by all four mosses grown in ionic rare earth tailings, this core microbiome could represent a large fraction (28.2% and 38.7%, respectively) of all sequence reads. The bacterial population and representation are the most abundant, which mainly includes Sphingomonas, Clostridium_sensu_stricto_1, and unclassified filamentous bacteria and chloroplasts, while the fungi population is relatively singular. The results also show that biocrust dominated by moss has a positive effect on soil microbe activity and soil nutrient conditions. Overall, these findings emphasize the importance of developing moss-dominated biocrusts as hotspots of ecosystem functioning and precious microbial genetic resources in degraded rare-earth mining areas and promoting a better understanding of biocrust ecology in humid climates under global change scenarios

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Abstract Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error

Nonlinear forces in urban thermal environment using Bayesian optimization-based ensemble learning

Urbanization witnessed unprecedented development globally, which causes citizens and urban temperature to become increasingly intertwined. Although researchers were interested in the field, most studies focused on holistic linear links between the characteristics of the urban built-up environment and temperature. The study used Bayesian optimization ensemble learning and Shapley value to decouple the urban thermal environment by Landsat satellite data. This work's novelties reveal the specific driving effect of different value ranges of urban features in the overall process on the urban thermal environment and advancing an optimum observation buffer zone of the urban surface temperature. The study's results were only for daytime and Beijing scope. The following are the main findings: (1) The 2 km observation buffer zone is best to analyze the urban thermal environment for this dataset. (2) The ecological environment factors have a more significant effect on the urban temperature than the urban morphology factors. (3) In summer, when the vegetation coverage exceeds 58.1%, every 10% increase could reduce the temperature by 0.84 °C. In contrast to summer, when vegetation coverage exceeds 64.7% and 73.2%, respectively, in spring and fall, there will be a significant marginal utility. (4) The effect of the building height has seasonal variations. It has the greatest cooling effect in the spring when the height is between 18 m and 75 m, and the daytime surface temperature at the time of Landsat overpass will drop by 1.25 °C. These findings will aid in understanding how building construction influences urban surface temperature and provide statistical support for planners