Environmental Research最新文献

Mangrove forestation is one of the most efficient forestry practices for carbon sequestration. This study developed a machine learning framework that integrated the random forest algorithm, SHapley Additive exPlanations (SHAP), and partial dependence plots (PDP) to assess global mangrove cover potential and its drivers, utilizing a suite of 48 environmental layers encompassing climatic, topographic, soil, and marine characteristics. Based on the mangrove cover potential, this study quantified the mangrove forestation potential under socioeconomic and ecological land-use constraints, as well as the carbon storage potential of forestation potential. The result showed that there is 156,682 km² of mangrove forestation potential under current climate conditions. When assessing the distribution of mangrove forestation potential across Marine Ecoregions of the World (MEOW) provinces, MEOW ecoregions, and countries, the greatest forestation capacity is observed in the Tropical Northwestern Atlantic, Amazonia, and Indonesia. SHAP and PDP results revealed that soil saturated water content and distance to sea are the key factors controlling mangrove cover potential. Under contrasting shared socioeconomic paths (SSP1-2.6 and SSP5-8.5), mangrove cover potential shows a general increase due to climate changes. However, under SSP5-8.5, sea-level rise alone could reduce the current forestation potential by 26,820 km². Furthermore, only 19,361 km² of the current forestation potential coincides with areas where future cover potential is projected to increase across both scenarios, indicating that the synergistic enhancement effect brought about by climate change on the forestation results is limited. From a national perspective, the five countries with the highest carbon storage potential from mangrove forestation are Indonesia, Brazil, Australia, Mexico, and the Philippines, with 1.016, 0.514, 0.409, 0.391, and 0.317 GtC, respectively. The global mangrove forestation potential map with clear spatial granularity provided in this study can offer important support for international-scale mangrove forestation.

Nitrate pollution in surface waters poses a dual challenge to ecosystem sustainability and human health, particularly in vulnerable plain basins with agricultural and urbanized regions. This study developed an integrated framework combining statistical and isotopic analyses, receptor modeling (Positive matrix factorization and MixSIAR), and probabilistic health risk assessment to investigate nitrogen pollution in a typical plain river basin of southwestern China. Results revealed that nitrate was the primary nitrogen pollutant in surface water, with higher concentrations observed in urbanized, agricultural, and confluence areas. Nitrification posed a significant influence on the nitrate concentration, whereas the effect of denitrification was considered negligible. Among diverse pollution sources, sewage discharge was the predominant contributor (dry season: 62.3 %, wet season: 65.2 %), followed by soil nitrogen and agricultural fertilizers. In addition, nitrate posed negligible non-carcinogenic risks to adults, with the maximum values of THI<1.00 (dry season: 0.44, wet season: 0.50). However, in the wet season, 1.90 % of the watershed posed potential health risks to children due to intense nitrification. A pronounced risk increase was identified in areas characterized by intensive anthropogenic activities and at river confluence zones. These findings revealed that nitrate contamination and associated health risks were substantially elevated in urban, agricultural, and confluence zones. This highlights the urgent need for strengthened sewage management, optimized fertilizer application, and targeted monitoring in high-risk zones. The proposed integrated framework provides a reliable approach for nitrate source identification and risk evaluation in plain basins, while providing effective guidance for local governments and policymakers in nitrate mitigation and sustainable development of water resources.

Marine ecosystems are undergoing unprecedented degradation, making ecological restoration an essential active intervention to mitigate this decline. In recent years, Xiamen has continuously advanced multiple types of bay ecological restoration projects, accumulating substantial practical experience. From an ecosystem perspective, this study develops a three-dimensional evaluation framework encompassing ecosystem pattern, ecosystem quality, and ecosystem function. Coastal blue carbon indicators reflect the spatial distribution of mangroves and other coastal vegetation; dissolved oxygen and chemical oxygen demand represent improvements in water quality; and coastal vulnerability indicators measure the ecosystem's capacity for disaster prevention and mitigation. Using 2003, 2013, and 2023 as reference years, we systematically evaluated the ecological restoration outcomes in Xiamen Bay over the past two decades. The results show that ecological conditions in the Jiulong River Estuary, Huandong Sea Area, and Eastern Sea Area improved significantly from 2003 to 2013. From 2013 to 2023, restoration effects became more pronounced in the Western Sea Area, Huandong Sea Area, and Eastern Sea Area, while restoration in the Dadeng Sea Area lagged due to human disturbances. Quantitative analysis indicates that restoration effectiveness increased by 60.4 % between 2003 and 2013, and by 23.6 % from 2013 to 2023, with an overall improvement of 98.2 % across the 20-year period. These findings highlight the long-term positive impacts of ecological restoration. Future efforts should focus on strengthening resilience in climate-vulnerable areas, enhancing long-term water quality monitoring and dynamic evaluation, and advancing the integration of restoration with infrastructure development to ensure sustainable and effective outcomes.

The concrete industry urgently requires innovative carbon management strategies to mitigate its substantial CO₂ footprint. Conventional carbonation curing is often constrained by equipment requirements and limited applicability to precast components, highlighting the need for alternative solutions suitable for cast-in-place concretes. This study introduces a novel method of incorporating solid CO₂ (dry ice) into hybrid alkaline cement (HAC) systems, enabling simultaneous performance enhancement and carbon storage. HAC mixtures containing 0-15% dry ice were prepared and systematically investigated in terms of hydration kinetics, mechanical strength, durability, and phase evolution. Isothermal calorimetry, XRD, TG, FTIR, and SEM were employed to reveal the mechanisms underlying the observed changes. Results demonstrate that dry ice moderates system alkalinity, promotes clinker hydration, and induces early precipitation of carbonates that subsequently transform into carbonaluminate phases. At an optimal dosage of 10%, compressive strength increased by 37.45% and surface resistivity by 22.69% at 28 days, accompanied by significant microstructural densification. However, excessive addition (15%) led to early temperature drops and reduced slag activation, which impaired overall performance. Sustainability analysis considering two boundary scenarios of CO₂ escape revealed that incorporating 10% dry ice reduced unit strength CO₂ emissions to 3.87-7.8 kg·CO₂/MPa, representing reductions of 51.9-3.23% compared with the control. These findings demonstrate that dry ice addition provides a low-cost, simple, and scalable route to integrate carbon storage with HAC development. This strategy offers new opportunities for achieving carbon-neutral cementitious materials with enhanced durability and structural performance, particularly in field applications where conventional carbonation curing is impractical.

Spheroidal carbonaceous particles (SCPs) and fossil fuel-derived soot (FF soot) in sediments are valuable proxies for reconstructing industrial emissions and understanding the multi-scale impacts of anthropogenic forcing on Earth systems. However, a systematic comparison of their initial deposition timing, flux peaks, and temporal patterns across lacustrine sedimentary records remains poorly constrained, leading to significant gaps in the understanding of the underlying drivers of these sedimentary signals. This study compared sediment records of these proxies from two maar lakes, Sihailongwan in northeastern China and Huguangyan in southeastern China, revealing how their signals exhibit synchronous patterns on a global scale while demonstrating complex heterogeneity at the regional scale due to differences in geographical location, climate systems, and industrialization pathways. Results showed that both SCP and FF soot fluxes in Sihailongwan began to rise in the 1950s, reaching a peak during China's rapid industrialization. In contrast, Huguangyan exhibited increasing fluxes only after the 1980s, synchronous with accelerated economic development in southeastern coastal regions, and culminating around 2010 CE, thereby reflecting intensified industrial activity and urbanization in this area. Notably, in both sediment records, the SCP peak occurred systematically earlier than the FF soot peak. This temporal offset likely reflects their representation of different industrialization phases and emission sources: SCPs derive mainly from industrial coal combustion, which peaked earlier, whereas FF soot also incorporates emissions from transportation fuels that rose later. Thus, these differences highlight the spatiotemporal evolution of energy structures and pollutant types throughout China's industrialization, especially those associated with black carbon. These findings offer important insights for selecting appropriate indicators to define the onset of the mid-20th century Anthropocene at varying spatial scales, and enhance our understanding of anthropogenic impacts from a micro-particle perspective.

Benthic algal proliferation in low-nutrient artificial channels poses emerging ecological risks, yet its mechanisms remain poorly understood compared with the well-studied cyanobacterial blooms in eutrophic water bodies. Using the Middle Route of the South-to-North Water Diversion Project (SNWDP-MR) as a case study, we investigated the spatiotemporal distribution patterns of benthic algae, identified environmental drivers, and integrated photosynthetic responses to develop models for predicting growth potential. Results showed seasonal succession from Bacillariophyta dominance in spring to Chlorophyta dominance in autumn. Biomass exhibited a spatial gradient along channel bends, with higher levels at the upstream than mid-bend and downstream sections. Multivariate analyses identified extracellular polymeric substances (EPS), soluble reactive phosphorus (SRP), nitrate nitrogen (NO₃^--N), and water temperature as primary drivers of benthic algal growth, with water temperature exerting the strongest influence on photosynthetic efficiency (28.47 %). Laboratory-simulated experiments revealed that algal growth rate of three typical benthic algae groups was significantly higher at 20 °C than at 15 °C (P < 0.05), with Cladophora-dominated communities exhibiting faster growth and greater sensitivity to environmental factors compared with those dominated by Bacillariophyta and Spirogyra. Mixed-effects models were developed on the basis of fluorescence parameters to predict benthic algal growth while incorporating environmental factors building on the observed positive correlations between chlorophyll fluorescence parameters and algal growth rate. This study highlights that fluorescence parameters, particularly F_v/F_m, have strong potential as early-warning indicators of benthic algal growth trends, providing a practical framework for risk management in large-scale water diversion systems.

Industrial solid wastes are increasingly used as alternative feedstocks for synthesising sulfoaluminate cement (SAC). However, their complexity in compositions leads to unstable performance. To optimise production, machine learning (ML) models are developed to predict the compressive strength of SAC pastes based on a dataset of 707 datapoints from literature. Distinct from traditional mineral-based approaches, this model incorporates multi-source factors including feedstock composition, clinker calcination temperature and duration time, gypsum type and content, specimen preparation conditions, and curing time. Single and ensemble ML approaches, including Random Forests (RF), Supporting Vector Regression (SVR), and Neural Network (NN) algorithms, are employed. The ensemble RF + NN model demonstrates higher accuracy (testing R² = 0.87) than the single models. Model-based interpretation reveals that feedstock composition is the foremost input feature group that accounts for 34.9 % importance, thereby validating the composition-driven prediction strategy. Moreover, the correlations of each input feature with compressive strength have been analysed. The ensemble ML model is validated through 14 independent experiments on SAC paste samples prepared exclusively from hazardous waste, with all prediction errors well below 10.82 %. This work provides a precise, data-driven tool for rapid feedstock screening and process optimisation, offering a labour-saving and cost-effective pathway to accelerate sustainable SAC production.