Search Results (27)

The problem of urban surface runoff (USR) treatment is associated with the presence of high concentrations of specific pollutants. One of these pollutants is petroleum product (PP), whose concentration depends on the season and the location of the formation of snow masses, meltwater, and rainwater. For USR treatment, it is possible to use very environmentally friendly and inexpensive technologies. The article discusses natural sorbents based on wood materials, which effectively remove dissolved petroleum products from water. Pine sawdust and shredded branches of maple, birch, and poplar are used as raw materials, which are waste products from the city’s woodworking enterprise and utilities. These materials were pre-microwave (MW) treated to improve their sorption properties. As a result of the experiment, it turned out that modified pine sawdust and crushed maple pinwheels proved to be the most effective sorbents. The maximum sorption capacity values were 0.689 mg/g and 0.952 mg/g for pine and maple sorbents, respectively. This article proposes schemes for filtering devices that can be used in practice in an urban environment. Full article

Societies are aiming to have a higher ecological consciousness in wastewater treatment operations and achieve a more sustainable future. With this said, global demands for larger quantities of resources and the consequent waste generated will inevitably lead to the exhaustion of current municipal wastewater treatment works. The utilization of biosolids (particularly microbial proteins) from wastewater treatment operations could generate a sustainable bio-adhesive for the wood industry, reduce carbon footprint, mitigate health concerns related to the use of carcinogenic components, and support a more circular economic option for wastewater treatment. A techno-economic analysis for three 10 MGD wastewater treatment operations producing roughly 11,300 dry pounds of biosolids per day, in conjunction with co-feedstock defatted soy flour protein at varying ratios (i.e., 0%, 15%, and 50% wet weight), was conducted. Aspen Capital Cost Estimator V12 was used to design and estimate installed equipment additions for wastewater treatment plant integration into an urban biorefinery process. Due to the mechanical attributes and market competition, the chosen selling prices of each adhesive per pound were set for analysis as USD 0.75 for Plant Option P1, USD 0.85 for Plant Option P2, and USD 1.00 for Plant Option P3. Over a 20-year life, each plant option demonstrated economic viability with high NPVs of USD 107.9M, USD 178.7M, and USD 502.2M and internal rates of return (IRRs) of 24.0%, 29.0%, and 44.2% respectively. The options examined have low production costs of USD 0.14 and USD 0.19 per pound, minimum selling prices of USD 0.42–USD 0.51 per pound, resulting in between 2- and 4-year payback periods. Sensitivity analysis shows the effects biosolid production fluctuations, raw material market price, and adhesive selling price have on economics. The results proved profitable even with large variations in the feedstock and raw material prices, requiring low market selling prices to reach the hurdle rate of examination. This technology is economically enticing, and the positive environmental impact of waste utilization encourages further development and analysis of the bio-adhesive process. Full article

It is essential to emphasize the significant impacts of climate change, which are evident in the form of severe and prolonged droughts, hurricanes, snowstorms, and other climatic disturbances. These challenges are particularly pronounced in urban environments and among human populations. The situation is further aggravated by the increasing utilization of available open spaces for residential and industrial development, leading to heightened energy consumption, elevated pollution levels, and increased carbon emissions, all of which negatively affect public health. The primary objective of this review article is to provide a comprehensive evaluation of current research, with a particular focus on the innovative use of residual biomass from urban vegetation for biochar production in the United States. This research entails an exhaustive review of existing literature to assess the implementation of a carbon-negative wood biomass biochar system as a strategic approach to sustainable urban management. By transforming urban wood waste—including tree trimmings, construction debris, and storm-damaged timber—into biochar through pyrolysis, a thermochemical process that sequesters carbon while generating renewable energy, we can leverage this valuable resource. The resulting biochar offers a range of co-benefits: it enhances soil health, improves water retention, reduces stormwater runoff, and lowers greenhouse gas emissions when applied in urban green spaces, agriculture, and land restoration projects. This review highlights the advantages and potential of converting urban wood waste into biochar while exploring how municipalities can strengthen their green ecosystems. Furthermore, it aims to provide a thorough understanding of how the utilization of woody biomass biochar can contribute to mitigating urban carbon emissions across the United States. Full article

This study models changes in land cover and land use in the intermediate city of Puerto Montt, Chile, up to 2050. Three distinct time periods—1988, 2005, and 2020—were used to examine Puerto Montt’s urban growth during these years. These periods were described using the Local Climate Zones (LCZ) technique. Urban growth scenarios were simulated using the Patch-generating Land Use Simulation (PLUS) model. Using Machine Learning (ML) techniques, this model has been widely utilized to explain how urban growth patterns have evolved based on the dynamics that drive changes in land use and land cover. Three scenarios were developed for this study: Business-As-Usual (BAU) (S1), Urban-Regional Planning (S2), and Conservationist (S3). According to the findings, Puerto Montt is predicted to undergo morphological changes by 2050, shifting from rural areas primarily composed of woods and agricultural land to open, low-density, low-rise areas outside the municipal limits set by the Communal Regulatory Plans. According to this study, Puerto Montt’s relative entropy level was high, ranging from 0.87 to 0.96, with a maximum value of 1.00 by 2050. These findings are anticipated to provide planners and decision-makers with further knowledge on the territorial design of upcoming urban areas. Full article

In the context of globalization and cultural diversity, the former residences of historical dignitaries in townships hold profound historical and cultural value, making them an important part of cultural heritage. However, as urbanization accelerates, these former residences encounter numerous challenges, necessitating a heightened focus on their protection and restoration. In this study, we utilized Wu’s Juren Mansion in Fujian as a focal point to delve deeply into the restoration design of the former residences of historical dignitaries in townships. This study covers the basic information, historical evolution, and surrounding environment of the building and investigated its damage in detail—including damage to the gatehouse, corridor, main building, roll roof, and walls—classified and counted the defect locations, and deeply analyzed the causes of damage. Based on relevant laws and regulations, we determined the nature, design principles, and foundation of the restoration project. We then formulated specific restoration measures, such as repairing and maintaining roofs, wooden structures, and walls, as well as measures for wood selection, adhesive use, and termite control. The restoration strategies and design schemes proposed in this study can effectively eliminate safety hazards, preserve the original style of the building to the greatest extent, and maintain historical and cultural value. At the same time, this research provides a reference example for the protection and development of former residences of historical dignitaries in townships and promotes the sustainable development of rural cultural heritage. Full article

The increasing frequency and intensity of wildfires are affecting the use of wood products in rural areas as well as at the wildland–urban interface. The enhancement in wood products’ reaction/resistance to fire is a concern often raised by national authorities. In the present study, different fire protection measures were applied to utility wood poles aiming to protect them from wildfires, ensuring their reuse in safe conditions while preventing them from contributing to the propagation of forest fires, particularly surface fires. Two of the solutions tested were based on intumescent paints, while the other one involved a system that completely covers the poles’ exteriors (a fabric-protection layer mechanically applied to the surface of the pole). These solutions were initially assessed in small-scale laboratory tests. Following these initial tests, a selected solution based on fabric protection was tested under simulated wildland fire conditions. The results obtained showed that fabric-based protection delivered satisfactory results, being easily applied on site, allowing the protection of poles already in service and the replacement of fire protection devices after a fire occurs. Full article

Black elder, Sambucus nigra, is a non-native but now partly naturalized shrub in Sweden; it has been cultivated here at least since the Middle Ages. Previously, this plant was associated with a supernatural being to whom sacrifices were made, and its fruits were used in folk medicine and wood for fuel and crafts. Traditional economic uses vanished with industrialization and urbanization and black elder was mostly planted as an ornamental shrub in urban parks. At the end of the 1970s, however, it made a sudden comeback: city dwellers started to gather flowers to make a refreshing non-alcoholic cordial. This diachronic study of Sambucus nigra spanning over a millennium reflects various attitudes and uses within the context of a changing human society. In addition to the simple but popular cordial, side dishes and desserts made of its fragrant flowers are becoming increasingly popular in modern Swedish cuisine. Globally it has also been (re)discovered and the utilization of this plant is growing: its flowers are used to add flavor to soft drinks, salads, desserts and various dishes; berries are used for medicine and in cooking, especially with meats, and its future uses seem to be limited only by human imagination. Full article

Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use of their respective advantages and effectively mitigates the limitations of GNSS positioning. However, the GNSS/INS integration faces significant challenges in complex and harsh urban environments. In recent years, the geometry between the user and the satellite has been effectively improved with the advent of lower-orbits and faster-speed Low Earth Orbit (LEO) satellites. This enhancement provides more observation data, opening up new possibilities and opportunities for high-precision positioning. Meanwhile, in contrast to the traditional extended Kalman filter (EKF) approach, the performance of the LEO-enhanced GNSS/INS tightly coupled integration (TCI) can be significantly improved by employing the factor graph optimization (FGO) method with multiple iterations to achieve stable estimation. In this study, LEO data and the FGO method were employed to enhance the GNSS/INS TCI. To validate the effectiveness of the method, vehicle data and simulated LEO observations were subjected to thorough analysis. The results suggest that the integration of LEO data significantly enhances the positioning accuracy and convergence speed of the GNSS/INS TCI. In contrast to the FGO GNSS/INS TCI without LEO enhancement, the average enhancement effect of the LEO is 22.16%, 7.58%, and 10.13% in the north, east, and vertical directions, respectively. Furthermore, the average root mean square error (RMSE) of the LEO-enhanced FGO GNSS/INS TCI is 0.63 m, 1.21 m, and 0.85 m in the north, east, and vertical directions, respectively, representing an average improvement of 41.91%, 13.66%, and 2.52% over the traditional EKF method. Meanwhile, the simulation results demonstrate that LEO data and the FGO method effectively enhance the positioning and convergence performance of GNSS/INS TCI in GNSS-challenged environments (tall buildings, viaducts, underground tunnels, and wooded areas). Full article

Accurately estimating vegetation biomass in urban forested areas is of great interest to researchers as it is a key indicator of the carbon sequestration capacity necessary for cities to achieve carbon neutrality. The emerging vegetation biomass estimation methods that use AI technologies with remote sensing images often suffer from arge estimating errors due to the diversity of vegetation and the complex three-dimensional terrain environment in urban ares. However, the high resolution of Light Detection and Ranging (i.e., LiDAR) data provides an opportunity to accurately describe the complex 3D scenes of urban forests, thereby improving estimation accuracy. Additionally, deep earning foundation models have widely succeeded in the industry, and show great potential promise to estimate vegetation biomass through processing complex and arge amounts of urban LiDAR data efficiently and accurately. In this study, we propose an efficient and accurate method called 3D-CiLBE (3DCity Long-term Biomass Estimation) to estimate urban vegetation biomass by utilizing advanced deep earning foundation models. In the 3D-CiLBE method, the Segment Anything Model (i.e., SAM) was used to segment single wood information from a arge amount of complex urban LiDAR data. Then, we modified the Contrastive Language–Image Pre-training (i.e., CLIP) model to identify the species of the wood so that the classic anisotropic growth equation can be used to estimate biomass. Finally, we utilized the Informer model to predict the biomass in the ong term. We evaluate it in eight urban areas across the United States. In the task of identifying urban greening areas, the 3D-CiLBE achieves optimal performance with a mean Intersection over Union (i.e., mIoU) of 0.94. Additionally, for vegetation classification, 3D-CiLBE achieves an optimal recognition accuracy of 92.72%. The estimation of urban vegetation biomass using 3D-CiLBE achieves a Mean Square Error of 0.045 kg/m², reducing the error by up to 8.2% compared to 2D methods. The MSE for biomass prediction by 3D-CiLBE was 0.06kg/m² smaller on average than the inear regression model. Therefore, the experimental results indicate that the 3D-CiLBE method can accurately estimate urban vegetation biomass and has potential for practical application. Full article

The increasing scarcity of virgin natural resources and the need for sustainable waste management in densely populated urban areas have heightened the importance of developing new recycling technologies. One promising approach involves recycling agricultural waste in construction applications and transforming it into secondary products. This is anticipated to reduce the demand for new resources and lower the environmental impact, aligning with industrial ecology principles. Combined with a low carbon emission binder (i.e., alkali-activated), utilizing agro-waste to produce zero-cement particle boards is a promising method for green construction. Traditionally, particle boards are engineered from wood or agricultural waste products that are pressed and bonded with a binder, such as cement or synthetic resins. However, alternative binders replace cement in zero-cement particle boards to address environmental concerns, such as the carbon dioxide emissions associated with cement production. This study investigated the effects of accelerated aging on the performance of alkali-activated agro-waste particle boards. Accelerated aging conditions simulate natural aging phenomena. Repeated wetting–drying and freezing–thawing cycles increased water absorption and thickness swelling and reduced flexural strength. The thermal performance of the alkali-activated particle boards did not exhibit significant changes. Hence, it was confirmed that agro-waste has a high potential for utilization in producing particle boards provided that the working environment is carefully selected to optimize performance. Full article

The objective of this paper is to tackle the issue of the degraded navigation accuracy of the inertial navigation system/global navigation satellite system (INS/GNSS) integrated navigation system in urban applications, especially under complex environments. This study utilizes historical state estimates and proposes a multi-step pseudo-measurement adaptive Kalman filter (MPKF) algorithm based on the filter performance evaluation. First, taking advantage of the independence between INS and GNSS, the enhanced second-order mutual difference (SOMD) algorithm is utilized for estimating the noise variance of the GNSS, which is decoupled from the estimate error of state and used as a module for filter performance evaluation. Then, the construction of the proposed method is presented, together with the analysis of the noise variance of multi-step pseudo-measurement. Ultimately, the efficacy of the MPKF is confirmed through a real-world vehicle experiment involving a tightly-coupled INS/GNSS integrated navigation application, demonstrating a noteworthy enhancement in navigation precision within densely wooded and built-up areas. Compared to the standard EKF and enhanced redundant measurement-based adaptive Kalman filter (ERMAKF), the proposed algorithm improves the positioning accuracy by 48% and 34%, velocity accuracy by 50% and 35%, and attitude accuracy by 38% and 48%, respectively, in the urban building segment. Full article

Background: The development of laser measurement techniques is of great significance in forestry monitoring and park management in smart cities. It provides many conveniences for improving landscape planning efficiency and strengthening digital construction. However, capturing 3D point clouds in large-scale landscape environments is a complex task that generates massive amounts of unstructured data with characteristics such as randomness, rotational invariance, sparsity, and serious barriers. Methods: To improve the processing efficiency of intelligent devices for massive point clouds, we propose a novel deep learning neural network based on a multi-feature aggregation strategy. This network is designed to divide 3D laser point clouds in complex large-scale scenarios. Firstly, we utilize multiple terrestrial laser sensors to collect a large amount of data in open scenes such as parks, streets, and forests in urban environments. These data are integrated into a practical database called DMSdataset, which contains different information variables, densities, and dimensions. Then, an automatic block integrated with a multi-feature extractor is constructed to pre-process the unstructured point cloud data and standardize the datasets. Finally, a novel semantic segmentation framework called PointDMS is designed using 3D convolutional deep networks. PointDMS achieves a better segmentation performance of point clouds with a lightweight parameter structure. Here, “D” stands for deep network, “M” stands for multi-feature, and “S” stands for segmentation. Results: Extensive experiments on self-built datasets show that the proposed PointDMS achieves similar or better performance in point cloud segmentation compared to other methods. The overall identification accuracy of the proposed model is up to 93.5%, which is a 14% increase. Particularly for living wood objects, the average identification accuracy is up to 88.7%, which is, at least, an 8.2% increase. These results effectively prove that PointDMS is beneficial for 3D point cloud processing, division, and mining applications in urban forest environments. It demonstrates good robustness and generalization. Full article

Shea is an agroforestry tree species known primarily for its rich butter, which contains stearin and tocopherol, and has ultraviolet ray absorption property; it is used in cooking, body care and traditional medicines. This tree is, however, uncultivated and collection of its nuts by rural dwellers is threatened by increased urbanization with its accompanying land use pressure and the need for fuel wood for rural households. There is also increased demand for shea products worldwide necessitating the need for shea improvement strategies. At the apex of this improvement program lies the need for germplasm collection, characterization, conservation and utilization. In order to conserve elite shea materials amidst dwindling shea populations threatened by climate change, there is a need to develop shea germplasm banks based on the representation of genetic and phenotypic variation focusing on known traits. The objective of the study was to evaluate 282 shea accessions for germplasm conservation and to determine the genetic diversity of the collected materials to inform future collections and drive crop improvement strategies. Leaf traits were used to differentiate and group the selected materials. Leaf length ranged between 16.83 cm and 30.85 cm, and leaf blade length ranged between 12.28 cm and 20.68 cm. Petiole length varied between 5.53 cm and 10.2 cm and the blade to petiole ratio was from 1.41 to 2.69. Correlation studies revealed significant negative correlation between the latitude of collection and all leaf traits measured. There was significant positive correlation between blade length and petiole length (0.57), blade length and total leaf length (0.87) and petiole length and breadth (0.49). The collected materials were grouped at 90% into two, based on the morphological descriptors studied. Three different approaches were employed to genetically analyze the materials based on single nucleotide polymorphic markers (SNP). A phylogenetic tree was constructed based on the SNPs generated; this grouped the materials into three, with various subgroups. Principal coordinate analysis also produced three distinct groups with groupings not based on geographical area of collection. Discriminant analysis of principal components (DAPC) also confirmed three groupings. The genetic diversity of the collection was very low (Hs) = 0.0406, which is an indication of potential inbreeding within the shea populations. To conclude, there was higher variation within locations than between locations. Full article

Climate change and the subsequent increase in global mean air temperature already present a significant impact on forest vegetation. Especially in the near future, several forest species are expected to be in danger of extinction or compelled to migrate to colder regions. Some common species will be replaced by highly climate-tolerant species, primarily exotic plants, among others. The tree of Ailanthus, or “tree-of-heaven”, as it is widely known, constitutes a rapidly growing plant, considered to be native to parts of China, while since the middle of the 18th century, it gradually spread to Europe and North America. This species demonstrates a preference for warmer, drier environments, although it can also survive in a variety of habitats and endure pollution of urban areas. It is a species with several uses, such as for animal feed, fuel, timber, pharmaceutical applications, etc., while its suitability for specialized applications of high-added-value is constantly being investigated. Its wood has a desirable appearance and characteristics that are comparable to those of other hardwood species of similar densities/weight. This article discusses some of the most important characteristics of Ailanthus wood and presents a comprehensive and constructive review of the chemistry, pharmacology, traditional and innovative uses, quality control, biological resistance, potential utilization in bioenergy and biofuels and wood products (e.g., wood-based panels, other advanced structure materials, etc.), use challenges and limitations, in order to contribute to the utilization potential assessment of this species biomass. Full article

Household air pollution (HAP) from biomass fuels significantly contributes to cardio-respiratory morbidity and premature mortality globally. Particulate matter (PM), one of the pollutants generated, remains the most accurate indicator of household air pollution. Determining indoor air concentration levels and factors influencing these levels at the household level is of prime importance, as it objectively guides efforts to reduce household air pollution. This paper describes household factors associated with increased PM_2.5 levels in Zimbabwean rural household kitchens. Our HAP and lung health in women study enrolled 790 women in rural and urban households in Zimbabwe between March 2018 and December 2019. Here, we report data from 148 rural households using solid fuel as the primary source of fuel for cooking and heating and where indoor air samples were collected. Data on kitchen characteristics and practices were collected cross-sectionally using an indoor walk-through survey and a modified interviewer-administered questionnaire. An Air metrics miniVol Sampler was utilized to collect PM_2.5 samples from the 148 kitchens over a 24 h period. To identify the kitchen features and practices that would likely influence PM_2.5 concentration levels, we applied a multiple linear regression model. The measured PM_2.5 ranged from 1.35 μg/m³ to 1940 μg/m³ (IQR: 52.1–472). The PM_2.5 concentration levels in traditional kitchens significantly varied from the townhouse type kitchens, with the median for each kitchen being 291.7 μg/m³ (IQR: 97.2–472.2) and 1.35 μg/m³ (IQR: 1.3–97.2), respectively. The use of wood mixed with other forms of biomass was found to have a statistically significant association (p < 0.001) with increased levels of PM_2.5 concentration. In addition, cooking indoors was strongly associated with higher PM_2.5 concentrations (p = 0.012). Presence of smoke deposits on walls and roofs of the kitchens was significantly associated with increased PM_2.5 concentration levels (p = 0.044). The study found that kitchen type, energy type, cooking place, and smoke deposits were significant predictors of increased PM₂.₅ concentrations in the rural households. Concentrations of PM_2.5 were high as compared to WHO recommended exposure limits for PM_2.5. Our findings highlight the importance of addressing kitchen characteristics and practices associated with elevated PM_2.5 concentrations in settings where resources are limited and switching to cleaner fuels may not be an immediate feasible option. Full article