Chunyu Pan

Bamboo-based engineered products have attracted increasing attention because of their renewable origin and long-term carbon storage potential. However, conventional life cycle assessment (LCA) mainly evaluates greenhouse gas emissions and cannot quantify how efficiently biomass-derived carbon is retained during manufacturing. Therefore, this study proposes carbon transfer efficiency (CTE) as a complementary indicator for assessing retained biogenic carbon in bamboo-based engineered products. Based on plant-scale manufacturing data from a representative bamboo scrimber factory in China, CTE was quantified together with the cradle-to-gate carbon footprint. Carbon loss distribution among manufacturing stages and sensitivity to key process parameters were further analyzed. Results showed that 65–78% of the initial biogenic carbon was retained in the final product. Unit separation and secondary machining accounted for more than 70% of total carbon loss, whereas adhesive application and hot pressing dominated greenhouse gas emissions but caused little direct carbon loss. Material-loss-related parameters exhibited substantially greater influence on CTE than energy-related parameters. These findings demonstrate that carbon retention efficiency and greenhouse gas emissions characterize different dimensions of environmental performance. Integrating CTE with LCA provides a more comprehensive framework for evaluating carbon storage and low-carbon manufacturing of bamboo-based engineered products.

With rapid urbanization, street trees have become an important part of urban greening. To investigate the effects of street trees on PM2.5 and PM10 pollution in summer in Hangzhou, 29 urban roads were selected. Vehicle-mounted LiDAR and a mobile environmental monitoring station were used to synchronously measure street tree parameters, road spatial structure, meteorological conditions, and traffic volume. The results showed significant spatiotemporal variations in PM2.5 and PM10 concentrations: Qiantang District (industrial zone) had the most severe pollution, while Xihu District (scenic region) had the lowest. Their temporal variation curves were highly consistent, indicating common sources. Street Trees exerted significant effects under two scenarios: W/H < 0.6 with traffic volume < 1500 vehicles/2h, and W/H > 1.2 with traffic volume > 1500 vehicles/2h. However, their effects had a threshold: under W/H = 0.6-1.2 and traffic volume > 1500 vehicles/2h, the effect was superior to high tree density only when street tree volume was 8000–16000 m³; excessive volume exacerbated pollution. Linear regression models for PM2.5 (F = 21.591, p < 0.001, adjusted R² = 0.290) and PM10 (F = 21.813, p < 0.001, adjusted R² = 0.292) showed that road volume and fuel vehicle traffic were extremely significantly positively correlated with PM concentrations (p < 0.001), while street tree volume and new energy vehicle traffic were extremely significantly negatively correlated (p < 0.001). These findings highlight conditional and threshold effects of street trees on PM pollution, informing evidence-based road design and urban greening to enhance air quality in urbanizing regions.

To quantitatively assess the environmental impact of producing a typical bamboo-based fiber composite material—bamboo scrimber (BS)—and to explore pathways for low-carbon optimization, this study adopts the Life Cycle Assessment (LCA) method with a focus on carbon footprint analysis. Using the actual production process of an enterprise as a case study, field data were collected and analyzed for bamboo scrimber with a nominal thickness of 1.5 cm. The results show that the carbon footprint of 1 m² of this product is 3.11 kg CO₂-eq, with the manufacturing stage contributing the highest emissions at 1.45 kg CO₂-eq. The primary source of carbon emissions is steam consumption, mainly occurring during the carbonization and drying of bamboo bundles. Therefore, optimizing these stages is crucial for reducing the overall carbon footprint of the product. This study provides a scientific basis for the sustainable development of bamboo-based fiber composite materials and offers practical recommendations for improving their environmental performance in production.

Forest ecosystems are crucial in mitigating air pollution and improving air quality. Therefore, investigating the relationships between air quality, forest structure, and environmental factors in different forest types is of significant importance. This study conducted three months of continuous monitoring (June–September 2023) of air quality factors (particulate matter (PM_2.5 and PM₁₀), ozone (O₃), and negative air ions (NAI)) and environmental factors (air temperature (TA), relative humidity (RH), light intensity (LI), and wind speed (WS)) in four subtropical forest types, along with vegetation characteristic surveys. The effects of forest structure and environmental factors on air quality were determined by correlation and multiple regression analysis. The results showed that the forest air quality is at its best in July during the summer season. Concentrations of particulate matter (PM) and ozone (O₃) in mixed coniferous and broadleaf forests (MCB), as well as deciduous broadleaf forests (DB), are lower than those in moso bamboo forests (MB) and evergreen broadleaf forests (EB). The troughs of PM concentrations occur in the early morning (4:00–6:00), while the troughs of O₃ concentrations occur in the early morning (4:00–6:00) and in the evening (18:00). NAI concentrations were highest in DB (1287 ions/cm³), followed by MCB (1187 ions/cm³), MB (896 ions/cm³), and EB (584 ions/cm³), with NAI concentrations peaking between 14:00 and 16:00. PM concentrations in forest air were primarily influenced by stand density (SD) and the Shannon–Wiener index of herbaceous layer (SWH) (p < 0.05); ozone concentrations were significantly affected by tree height (TH) and canopy density (CD) (p < 0.05); and NAI concentrations were primarily related to TH and diameter at breast height (DBH). Air particulate matter concentrations were negatively affected by TA and RH (p < 0.01), and ozone concentrations were negatively influenced by RH and WS and were positively influenced by TA. TA has a direct and significant positive effect on the NAI concentration (p < 0.01), and RH indirectly influences the changes in NAI concentration through its interaction with TA. This study provides new insights for vegetation optimization in forest parks and planning forest health-promoting activities for sub-healthy populations.

Zhejiang Province, a pivotal economically developed region within China’s Yangtze River Delta, requires systematic investigation of spatiotemporal vegetation dynamics and their drivers to formulate targeted ecological protection policies and optimize vegetation restoration strategies. Utilizing the Google Earth Engine (GEE) platform, this study applied the Kernel Normalized Difference Vegetation Index (kNDVI) to assess vegetation responses to climate variability and human activities in Zhejiang Province from 2000 to 2022. Analytical methods included simple linear regression, Theil Sen trend analysis (Sen), Mann Kendall test (MK), Hurst index, partial correlation analysis, and correlation analysis. The results show: (1) The kNDVI exhibited a significant upward trend (0.001/year), covering 61.5% of the province. The Hurst index analysis revealed that 69.1% of vegetation changes exhibited anti-sustainability characteristics, with future vegetation degradation areas (56.4%) projected to exceed improvement areas (28.1%). (2) Human activities (57.11%) contributed more to kNDVI changes than climate change (42.89%). (3) Against the backdrop of climate change, kNDVI demonstrated a positive partial correlation with temperature (coefficient: 0.44) but exhibited a negative correlation with precipitation (coefficient: −0.056), confirming temperature as the dominant climatic driver. Overall, vegetation dynamics in Zhejiang Province from 2000 to 2022 were jointly driven by climate change and human activities.

Forest soil respiration plays a crucial role in the global carbon cycle. However, accurately estimating regional soil carbon fluxes is challenging due to the spatial heterogeneity of soil respiration at the stand level. This study examines the spatial variation of soil respiration and its driving factors in subtropical coniferous and broad-leaved mixed forests in southern China, aiming to provide insights into accurately estimating regional carbon fluxes. The findings reveal that the coefficient of variation (CV) of soil respiration at a scale of 50 m × 50 m is 18.82%, indicating a moderate degree of spatial variation. Furthermore, 52% of the spatial variation in soil respiration can be explained by the variables under investigation. The standardized total effects of the main influencing factors are as follows: soil organic carbon (0.71), diameter at breast height within a radius of 5 m (0.31), soil temperature (0.27), and soil bulk density (−0.25). These results imply that even in relatively homogeneous areas with flat terrain, fine-scale soil respiration exhibits significant spatial heterogeneity. The spatial distribution of woody plant resources predominantly regulates this variation, with root distribution, shading effects, and changes in soil physical and chemical properties being the main influencing mechanisms. The study emphasizes the importance of simulations at different microscales to unravel the potential mechanisms governing macroscopic phenomena. Additionally, it highlights the need for incorporating a more comprehensive range of variables to provide more meaningful references for regional soil carbon flux assessment.