Carbon Density Research Articles

Quantifying soil organic carbon (SOC) density and stock in the Urmodi River watershed of Maharashtra, India: implications for sustainable land management

Soil Organic Carbon (SOC) plays a vital role in the global carbon cycle, affecting soil fertility and agricultural sustainability. Our study focused on areas with low SOC, where increasing its levels could enhance soil health and carbon management. We used an earth science approach to analyze SOC density and stock in different land classes of the Urmodi River watershed in Maharashtra, India. Using GIS, we estimated SOC from soil samples collected up to 20 cm depth, and produced maps of SOC, SOC density (SOCD), and SOC stock. Our results showed that natural vegetation areas, such as closed and open forests, had a higher nutritional status of SOC and SOC stock, indicating the importance of land cover history on soil health. We found the average SOC to be 1.4 g/kg−1, SOCD to be 57.36 kg/m−2, SOC stock to be 3.46 ton/ha−1. We also depicted the relationship between elevation and SOCD using a scatterplot, revealing the distribution of SOC across different elevations. Our study demonstrates the use of modern geoscientific analysis tools to understand the physical and chemical properties of soil, which can be useful in assessing soil health. Overall, our results provide valuable insights into the distribution of SOC and its relationship with other geo-chemical aspects at a regional scale.

Forest Clearing Dynamics and Its Relation to Remotely Sensed Carbon Density and Plant Species Diversity in the Puuc Biocultural State Reserve, Mexico

The Puuc Biocultural State Reserve (PBSR) is a unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on maintaining low-impact productive activities coordinated by multiple communal and private landowners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and their relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000–2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (±19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000–2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing changed over the years after the creation of the PBSR in 2011. However, an emergent hotspot analysis shows that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012–2021 period was less common than prior to 2011, and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, the results show that landowners outside the PBSR often clear forests with lower carbon density and species diversity than those inside the PBSR. This suggests that, compared to landowners outside the PBSR, landowners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012–2021 period, minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend continuing efforts to provide alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. Combining these models with continuous change detection datasets will allow for underlying ecological processes to be revealed and the generation of information better adapted to forest governance scales.

Zonal patterns and driving factors of soil organic carbon density in Zhejiang Province, China

The complex topography and intensive land use and land cover change (LUCC) might shift the spatial pattern of soil organic carbon (SOC) density. However, the effects of LUCC caused by development on the distribution and driving factors of SOC density remain unclear. As one of the most developed regions in China, Zhejiang Province has experienced intensive land use change and has various vegetation types, making it an ideal location to analyse the zonal pattern and driving factors of SOC density in terrestrial ecosystems. To do that, a dataset was built with data collected from existing literature. The average SOC density in Zhejiang Province was 69.75 Mg ha−1, ranging widely from 6.44 to 301.90 Mg ha−1 depending on land and vegetation types. We found that forests had the highest mean SOC density (78.30 Mg ha−1), while cropland had the lowest (45.69 Mg ha−1). Meanwhile, the SOC density of unmanaged land was significantly higher than that of managed land (P < 0.001). Additionally, SOC density varied greatly with longitude and altitude, showing a decreasing zonal pattern from west to east and from high to low altitude. The RF model indicated that vegetation type, stand age, mean annual temperature (MAT), soil pH, and mean annual precipitation (MAP) were the five most important factors affecting SOC density in this area, but the effects differed between managed and unmanaged lands. These results aid with understanding the dynamics of SOC pools in terrestrial ecosystems and inform policy-making to mitigate global climate change. These findings provide valuable insights into the dynamics of SOC pools in terrestrial ecosystems, enabling more effective policymaking to mitigate the impact of global climate change.

Predicting changes in the carbon stocks of bamboo forests in Japan from 1985 to 2005

ABSTRACT Bamboo forests are expected to play an important role in mitigating climate change. However, the capacity of bamboo forests to sequester and store carbon has not been assessed on a national scale in Japan. This study estimates the changes in the carbon stock of bamboo forests (CSbamboo) in Japan from 1985 to 2005. We compiled two data sets: (1) administrative statistical data on bamboo stand area (BSA) and (2) published data on stand-level aboveground biomass for Phyllostachys pubescens Mazel ex Houz. (n = 44) and Phyllostachys bambusoides Sieb. et Zucc. (n = 13). The BSA expanded from 147 kha in 1985 to 158 kha in 2005. The average carbon density (CD) of managed and abandoned stands were 84.9 Mg C ha−1 and 115.1 Mg C ha−1 for P. pubescens and 24.1 Mg C ha−1 and 46.4 Mg C ha−1 for P. bambusoides, respectively. The CD of P. pubescens stands in Japan was higher than that in other Asian countries and regions and was comparable to the CD of other forests in Japan. The area-based method indicated that the CSbamboo increased from 10.1 ± 2.6 Tg C to 13.9 ± 1.7 Tg C during the 20-year period, representing less than 1% of the total carbon pool of the forested area. The increase in CSbamboo was primarily caused by the synergistic effect of the abandonment and range expansion of P. pubescens forests. Our results suggest that P. pubescens forests are overstocked, and the increase in the CSbamboo is undesirable.

Soil quality assessment of lowland rice soil of eastern India: Implications of rice husk biochar application

The role of biochar in improving the soil properties of problem soils is well known, but its long term impact on lowland rice soil is not well recognized. The soil quality indicators of biochar applied lowland rice soil are not widely reported. We developed soil quality index (SQI) of a biochar applied lowland rice soil based on 17 soil properties (indicators). Field experimentation consisted of six treatments such as 0.5, 1, 2, 4, 8 and 10 t ha−1 of rice husk derived biochar (RHB) along with control. An overall SQI was calculated encompassing the indicators using multivariate statistics (principal component analysis) and non-linear scoring functions after generation of minimum data set (MDS). Sequential application of RHB improved the SQI by 4.85% and 16.02% with application of 0.5 t ha−1 and 10 t ha−1 RHB, respectively, over the recommended dose of fertilizer (control). PCA-screening revealed that total organic carbon (Ctot), zinc (Zn), pH and bulk density (BD) were the main soil quality indicators for MDS with 27.79%, 26.61%, 23.67% and 14.47% contributions, respectively. Apart from Ctot, Zn is one of the major contributors to SQI and RHB application can potentially be an effective agronomic practice to improve Zn status in lowland rice soil. The overall SQI was significantly influenced by RHB application even at 0.5 t ha−1. The present study highlights that application of RHB improves the soil quality even in fertile, well managed, lowland rice soil.

Effects of Nitrogen Deposition on Leaf Litter Decomposition and Soil Organic Carbon Density in Arid and Barren Rocky Mountainous Regions: A Case Study of Yimeng Mountain

The ecological impact of nitrogen (N) deposition has gained significance since the advent of the industrial revolution. Although numerous studies have examined the impact of N deposition on soil organic carbon (SOC), certain arid and barren rocky mountainous regions, which experience more pronounced N limitations, have been overlooked. This study was conducted in the Yimeng Mountains, examining eight treatments created by four N addition levels (0 kg N ha−1 yr−1, 50 kg N ha−1 yr−1, 100 kg N ha−1 yr−1 and 200 kg N ha−1 yr−1) and two tree species (Quercus acutissima Carruth. and Pinus thunbergii Parl.). The research revealed variations in the effect of N addition on leaf litter decomposition and SOC density (SOCD) between different tree species. Notably, N addition stimulated the decomposition of leaf litter from Quercus acutissima Carruth. However, the decomposition of Pinus thunbergii Parl. leaf litter was enhanced at N addition levels below 100 kg N ha−1 yr−1, while it was hindered at levels exceeding 100 kg N ha−1 yr−1. In the Quercus acutissima Carruth. forest, the N addition levels of 50 kg N ha−1 yr−1, 100 kg N ha−1 yr−1 and 200 kg N ha−1 yr−1 resulted in decreases in SOCD by 10.57%, 22.22% and 13.66%, respectively, compared to 0 kg N ha−1 yr−1. In the Pinus thunbergii Parl. forest, the N addition levels of 50 kg N ha−1 yr−1, 100 kg N hm−2 ha−1 and 200 kg N ha−1 yr−1 led to increases in SOCD by 49.53%, 43.36% and 60.87%, respectively, compared to 0 kg N ha−1 yr−1. Overall, N addition decreases the SOCD of Quercus acutissima Carruth., but it increases the SOCD of Pinus thunbergii Parl., attributed to the alteration in soil enzyme stoichiometry and nutrient cycling by N addition. This study fills a theoretical gap concerning leaf litter decomposition and SOC sequestration in arid and barren rocky mountainous regions under global climate change.

The Effect of Variable Parameters on Carbon Residue of Iraqi Vacuum Gas Oil using Ultrasound Techniques

An ultrasonic treatment was applied to the vacuum gas oil at intervals of 5 to 30 minutes, at 70°C. In this work, the improvement of the important properties of Iraqi vacuum gas oil, such as carbon residue, was studied with several parameter conditions that affect vacuum efficiency, such as sonication time (5, 10, 15, 20, 25, and 30) min, power amplitude (10–50%). After ultrasonic treatment, the carbon residue of vacuum gas oil was evaluated using a Conradson carbon residue meter (ASTM D189). The experiment revealed that the oil's carbon residue had decreased by 16%. As a consequence of the experiment It was discovered that ultrasonic treatment might reduce the carbon residual and density of oil samples being studied. It also noticed that the carbon residue reduced with increased ultrasonic treatment duration and power. The mechanical mixing and cavitation brought about by ultrasonic processing led to a number of modifications in the gas oil molecules. The properties of a typical molecular structure were altered on a microscale.

Photodissociation and photoionization of molecules of astronomical interest

Context. Vacuum-ultraviolet (VUV) photons are important drivers of chemical processes in space. Thus, it is important to accurately characterize and constrain photorates in different radiation fields, via the photodissociation and photoionization cross sections of individual atoms and molecules. These have been available in the Leiden VUV photodissocation and photoionization cross section database. Aims. Experimental and theoretical advances in the past decade or so have allowed multiple new cross sections to be obtained, particularly photoionization cross sections of radicals. The database is hereby updated by including these more recent cross sections and is also expanded with several astronomically relevant species. Methods. The cross sections have been used to calculate photodissociation and photoionization rates in several different radiation fields as well as from cosmic-ray-induced VUV fluxes. The reduction of rates in shielded regions was calculated as a function of dust, molecular and atomic hydrogen, atomic carbon, and self-shielding column densities. The relative importance of these shielding types is molecule and atom dependent, as well as the assumed dust absorbance. All the data are publicly available from the Leiden VUV cross section database. Results. The Leiden VUV cross section database has been updated with 14 new astrophysically relevant molecular species and 16 updates to previous entries. The database update is accompanied by a brief review of the basic physical processes, particularly photoionization processes which have not been reviewed in the context of previous database updates.

Effects of cropland reclamation on soil organic carbon in China's black soil region over the past 35 years.

The long-term use of cropland and cropland reclamation from natural ecosystems led to soil degradation. This study investigated the effect of the long-term use of cropland and cropland reclamation from natural ecosystems on soil organic carbon (SOC) content and density over the past 35 years. Altogether, 2140 topsoil samples (0-20 cm) were collected across Northeast China. Landsat images were acquired from 1985 to 2020 through Google Earth Engine, and the reflectance of each soil sample was extracted from the Landsat image that its time was consistent with sampling. The hybrid model that included two individual SOC prediction models for two clustering regions was built for accurate estimation after k-means clustering. The probability hybrid model, a combination between the hybrid model and classification probabilities of pixels, was introduced to enhance the accuracy of SOC mapping. Cropland reclamation results were extracted from the land cover time-series dataset at a 5-year interval. Our study indicated that: (1) Long-term use of cropland led to a 3.07 g kg-1 and 6.71 Mg C ha-1 decrease in SOC content and density, respectively, and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2) nearly 64% of cropland had a negative change in terms of SOC content from 1985 to 2020; (3) cropland reclamation track changed from high to low SOC content, and almost no cropland was reclaimed on the "Black soils" after 2005; (4) cropland reclamation from wetlands resulted in the highest decrease, and reclamation period of years 31-35 decreased when SOC density and SOC stock were 16.05 Mg C ha-1 and 0.005 Pg, respectively, while reclamation period of years 26-30 from forest witnessed SOC density and stock decreases of 8.33 Mg C ha-1 and 0.01 Pg, respectively. Our research results provide a reference for SOC change in the black soil region of Northeast China and can attract more attention to the area of the protection of "Black soils" and natural ecosystems.

Scale-dependent and driving relationships between spatial features and carbon storage and sequestration in an urban park of Zhengzhou, China

Research indicates that urban ecosystems can store large amounts of carbon. However, few studies have examined how the spatial features of park greenspace affect its carbon-carrying capacity, and how those effects vary with the spatial scale. Lidar point clouds and remote sensing images were extracted for the 196 ha green space in the China Green Expo to study carbon storage and sequestration in parks. Full subset regression, stepwise regression, HP analysis, and structural equation modeling were used to examine the scale dependency and the driving relationship between carbon storage and carbon sequestration in parks. The results show that the optimal statistical sample diameters for carbon density and carbon sequestration density in parks are 100 m. Under the influence of impermeable surfaces and water bodies, the statistical values of carbon density were minimized when the sample plot diameter was 700 m. Biodiversity and forest structure are the main drivers of carbon density, with the influence of water bodies being more prominent on a larger scale. Texture characteristics explain more carbon density than the vegetation index, and RVI could better explain the variation of carbon sequestration than NDVI. This study explores scaled changes in carbon density, carbon sequestration density in parks, and their driving relationships, which can aid in developing carbon sequestration strategies based on parks.

Impact of Amide Fertilizer on Carbon Sequestration under the Agroforestry System in the Eastern Plateau Region of India

Carbon sequestration is an important aspect of expelling greenhouse gases from the atmosphere and decelerating the rate of global warming. Agroforestry plays an important role in carbon sequestration. Keeping this in mind, the current study was carried out between 2017 and 2021 to assess the effect of integrated nutrient management on biomass production, carbon sequestration, and carbon credit in a mango and turmeric agroforestry system. The study used randomized block design (RBD) with four treatments and five replications. According to the findings of this study, the rate of fertilizer application has a significant impact on the growth of turmeric and mango crops. The physiochemical characteristics of soil show an improvement in soil composition with the application of urea (CO(NH2)2), single super phosphate [Ca(H2PO4)2.2H2O] 226 kg ha−1, MOP [KCl] 309 kg ha−1 100 kg ha−1. The carbon density of the agrihorticulture land use system was six to seven times higher than that of the open agriculture-based land use system. The highest turmeric production (8.98 t ha−1) was reported under the mango-turmeric system rather than turmeric alone (6.36 t ha−1) in the T2-N100kg treatment. Total biomass production (61.2 t ha−1 and 64.6 t ha−1), carbon stock (38.6 t ha−1 and 41.06 t ha−1), carbon sequestration (246.5 t ha−1 and 299.5 t ha−1), and carbon credit (246.57 credits and 299.5 credits) were found to be highest in mango and turmeric-based agroforestry land use system treatments T2-N100 kg and T3-N80 Kg, respectively. The net additional profit from the agrihorticulture land use system was 299.5 carbon credits, which is equivalent to 4,49,250 INR.

Seagrass meadow stability and composition influence carbon storage

ContextSeagrass ecosystems are lauded for storing organic carbon in underlying sediments, but storage is highly variable, even at relatively small spatial scales. While environmental setting and seagrass cover are known drivers of carbon storage capacity, it is unclear how other seagrass features such as species composition influence carbon storage, and whether historical vs. contemporary features are better predictors of storage.ObjectivesWe examined the influence of historical and contemporary seagrass variables on surface (0–10 cm) sediment organic carbon storage at the meadow-scale (~ 25 km2), in addition to the influence of environmental drivers. Our study area was located within a subtropical mixed-species seagrass meadow along a low-energy coastline in the northeastern Gulf of Mexico (Cedar Key, Florida, USA).MethodsWe derived historical metrics of seagrass cover and composition from 14-year seagrass monitoring datasets and measured surface sediment carbon densities and grain size, contemporary seagrass biomass and species composition, as well as environmental characteristics related to hydrology and physical disturbance (i.e., relative exposure, elevation, and distance to navigation channels). We assessed bivariate relationships between predictor variables and surface carbon densities with linear regression analyses and used path analysis to assess hypothesized relationships between a subset of predictor variables and carbon densities.ResultsWhile low relative to global values, surface carbon densities in Cedar Key seagrass meadows varied by an order of magnitude. Sediment grain size was strongly related to carbon densities, but environmental variables had only indirect effects on carbon densities. Historical seagrass cover, variability in cover, and species diversity were generally better predictors of storage than contemporary variables. Historical and contemporary species identity–specifically the presence of Thalassia testudinum–were also significant drivers of storage.ConclusionsIn Cedar Key, historically diverse and persistent seagrass meadows dominated by late-successional species contained the largest surface carbon stores. Our results highlight the importance of site history in terms of meadow stability (inversely measured as variability in cover) as well as species identity and diversity in enhancing surface carbon storage. The environmental variables we examined had comparatively weak effects on carbon densities, however, relative exposure and elevation may not be the most relevant hydrological drivers of carbon storage at the meadow scale. Together, these findings suggest that drivers of seagrass meadow carbon storage are context and scale dependent.

Advanced cycling ageing-driven circular economy with E-mobility-based energy sharing and lithium battery cascade utilisation in a district community

Electrochemical storage technologies are essential for transformation towards electrification in both building and transportation sectors. The high cost, intensified carbon density and performance ageing of electrochemical batteries prevent its sustainable applications. Many studies have explored the battery circular economy based on reusing retired EV batteries with economic and environmental competitiveness. However, few studies combine battery circular economy with E-mobility-based interactive renewable energy sharing network for sustainability, together with comprehensive considerations on dynamic battery performance and degradation. In this study, a building-vehicle energy interaction model and a battery circular economy model were developed to simulate the process of reusing retired EV batteries. Considering the inconsistency in the carbon intensity of battery operation, manufacturing and recycling phases, a lifecycle battery carbon intensity criterion is proposed to quantify the lifetime carbon of batteries, and assess the decarbonisation capability of different renewable sources, battery reusing and V2B/B2V interaction, respectively. Results show that reusing retired batteries from EVs to buildings can improve the demand coverage ratio (DCR) from 0.480 to 0.504 and the renewable penetration ratio (RPR) from 0.502 to 0.531, respectively. Furthermore, the battery carbon intensity cannot be offset unless both renewables and reused batteries are integrated. The case study demonstrates the decrease of battery carbon intensity from 1284.57 kg CO2,e/kWh to −720.79 kg CO2,e/kWh with renewable and reused batteries. Research results can provide innovative battery circular economy frameworks for electrification transformation, decarbonisation and sustainable development.

Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem

China’s forests play a vital role in the global carbon cycle through the absorption of atmospheric CO2 to mitigate climate change caused by the increase of anthropogenic CO2. It is essential to evaluate the carbon sink potential (CSP) of China’s forest ecosystem. Combining NDVI, field-investigated, and vegetation and soil carbon density data modeled by process-based models, we developed the state-of-the-art learning ensembles model of process-based models (the multi-model random forest ensemble (MMRFE) model) to evaluate the carbon stocks of China’s forest ecosystem in historical (1982–2021) and future (2022–2081, without NDVI-driven data) periods. Meanwhile, we proposed a new carbon sink index (CSindex) to scientifically and accurately evaluate carbon sink status and identify carbon sink intensity zones, reducing the probability of random misjudgments as a carbon sink. The new MMRFE models showed good simulation results in simulating forest vegetation and soil carbon density in China (significant positive correlation with the observed values, r = 0.94, P < 0.001). The modeled results show that a cumulative increase of 1.33 Pg C in historical carbon stocks of forest ecosystem is equivalent to 48.62 Bt CO2, which is approximately 52.03% of the cumulative increased CO2 emissions in China from 1959 to 2018. In the next 60 years, China’s forest ecosystem will absorb annually 1.69 (RCP45 scenario) to 1.85 (RCP85 scenario) Bt CO2. Compared with the carbon stock in the historical period, the cumulative absorption of CO2 by China’s forest ecosystem in 2032–2036, 2062–2066, and 2077–2081 are approximately 11.25–39.68, 110.66–121.49 and 101.31–111.11 Bt CO2, respectively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical CSindex covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83–72.22 Bt CO2, respectively. In the future, China’s forest ecosystem has a large CSP with a non-continuous increasing trend. However, the CSP should not be underestimated. Notably, the medium carbon sink intensity region should be the priority for natural carbon sequestration action. This study not only provides an important methodological basis for accurately estimating the future CSP of forest ecosystem but also provides important decision support for future forest ecosystem carbon sequestration action.

Biomass-derived hierarchically porous carbon-supported MnOx enable fast ion/electron transport at high operating voltages

Metal oxide/carbon composite systems have been investigated extensively for fabrication of supercapacitors. To obtain high capacitance, a thick pseudocapacitive layer loaded on a conventional carbon support inevitably leads to high dead volume and poor rate/power characteristics. Herein, we propose a strategy with biomass-derived hierarchical porous carbon as supports to bridge two critical but mutually exclusive properties of supercapacitors: high capacitance and rapid ion/charge transport. Highly dispersed deposition of nanosized MnO2 and MnO on hierarchically porous carbon were achieved through slow redox reaction controlled by gradual hydrolysis of fibrous polymers remaining in the hydrothermal carbon. High surface area, ultrathin pseudocapacitance layer, and wide voltage window (1.6–2 V) improve the synergy between the high energy density of MnOx and the high power density of porous carbon. The maximum energy density of 40.95 Wh kg−1 (at 180 W kg−1) and power density of 9000 W kg−1 (at 23.85 Wh kg−1) was realized for the assembled asymmetric/symmetric supercapacitors in aqueous electrolytes. More importantly, a cycle life comparable to that of carbon-based double-layer capacitors (95.6% capacitance retention after 10,000 charge/discharge cycles) could be achieved. Furthermore, density functional theory (DFT) was also conducted to elaborate the differences between manganese valence states for in-depth understanding. The characteristics of low cost, environmental friendliness, and excellent performance highlight the great promise of biomass waste in designing carbon supports for electrochemical applications.

R-MFNet: Analysis of Urban Carbon Stock Change against the Background of Land-Use Change Based on a Residual Multi-Module Fusion Network

Regional land-use change is the leading cause of ecosystem carbon stock change; it is essential to investigate the response of LUCC to carbon stock to achieve the strategic goal of “double carbon” in a region. This paper proposes a residual network algorithm, the Residual Multi-module Fusion Network (R-MFNet), to address the problems of blurred feature boundary information, low classification accuracy, and high noise, which are often encountered in traditional classification methods. The network algorithm uses an R-ASPP module to expand the receptive field of the feature map to extract sufficient and multi-scale target features; it uses the attention mechanism to assign weights to the multi-scale information of each channel and space. It can fully preserve the remote sensing image features extracted by the convolutional layer through the residual connection. Using this classification network method, the classification of three Landsat-TM/OLI images of Zhengzhou City (the capital of Henan Province) from 2001 to 2020 was realized (the years that the three images were taken are 2001, 2009, and 2020). Compared with SVM, 2D-CNN, and deep residual networks (ResNet), the overall accuracy of the test dataset is increased by 10.07%, 3.96%, and 1.33%, respectively. The classification achieved using this method is closer to the real land surface, and its accuracy is higher than that of the finished product data obtained using the traditional classification method, providing high-precision land-use classification data for the subsequent carbon storage estimation research. Based on the land-use classification data and the carbon density data corrected by meteorological data (temperature and precipitation data), the InVEST model is used to analyze the land-use change and its impact on carbon storage in the region. The results showed that, from 2001 to 2020, the carbon stock in the study area showed a downward trend, with a total decrease of 1.48 × 107 t. Over the course of this 19-year period, the farmland area in Zhengzhou decreased by 1101.72 km2, and the built land area increased sharply by 936.16 km2. The area of land transfer accounted for 29.26% of the total area of Zhengzhou City from 2001 to 2009, and 31.20% from 2009 to 2020. The conversion of farmland to built land is the primary type of land transfer and the most important reason for decreasing carbon stock. The research results can provide support, in the form of scientific data, for land-use management decisions and carbon storage function protections in Zhengzhou and other cities around the world undergoing rapid urbanization.

Changes in carbon stock in the Xing’an permafrost regions in Northeast China from the late 1980s to 2020

ABSTRACT The degradation of Xing’an permafrost affects the stability of carbon pool and carbon emissions of the hemiboreal ecosystem in Northeast China. Due to the lack of long-term monitoring and detailed research, changes in carbon stock in terrestrial ecosystems in the Xing’an permafrost regions in Northeast China remain little known. In this study, we conducted more accurate simulations for the aboveground biomass (AGB), belowground biomass (BGB), dead organic carbon (DOC), and soil organic carbon (SOC) in the top 0–30 cm in permafrost regions in Northeast China using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The model was applied using multi-period land use/land cover (LULC) and carbon density data from the late 1980s, 2000, 2010, and 2020. In Northeast China, the permafrost extent shrank rapidly by 108,600 km2 from 365,300 km2 in the 1980s to 256,700 km2 in the 2010s. The total carbon stock in permafrost regions in Northeast China was estimated at 4293.04 Tg C in the 1980s and at 4049.56 Tg C in the 2010s. Based on the permafrost extent of the 1980s, it was estimated that from the late 1980s to 2020, the LULC transformation resulted in the reduction of carbon stock by 33.53 Tg C, while the carbon fixation of vegetation growth increased by 1205.18 Tg C, i.e. a net carbon accumulation of 1149.15 Tg C. However, this estimate of the increasing carbon pool still awaits more systematic studies on the carbon budgets at larger soil depths of the top 2–3 m soils. This estimate can provide a timely preliminary estimate of the carbon pool as the baseline in the permafrost region of Northeast China for national and regional initiatives of carbon neutralization.

Forest Carbon Density Estimation Using Tree Species Diversity and Stand Spatial Structure Indices

The forest spatial structure and diversity of tree species, as the important evaluation indicators of forest quality, are key factors affecting forest carbon storage. To analyze the impacts of biodiversity indices and stand spatial structure on forest carbon density, five tree diversity indices were calculated from three aspects of richness, diversity and evenness, and three indices (Reineke’s stand density index, Hegyi’s competition index and Simple mingling degree) were calculated from stand spatial structure. The relationships between these eight indices and forest carbon density were explored using the Structural Equation Model (SEM). Then, these eight indices were used as characteristic variables to predict the aboveground carbon density of trees (abbreviated as forest carbon density) in the sample plots of the National Forest Resources Continuous Inventory (NFCI) in Shaoguan City in 2017. Multiple Linear Regression (MLR) and four typical machine learning models of Random Forest (RF), Tree-based Piecewise Linear Model (M5P), Artificial Neural Network (ANN) and Support Vector Regression (SVR) were used to predict the forest carbon density. The results show that: (1) Based on the analysis results of the structural equation model (SED), the species diversity and forest stand spatial structure have greater impacts on carbon density. (2) The R2 of all the five prediction models is greater than 0.6, among which the random forest model is the highest. (3) Based on the calculation results of optimal model of RF, the mean forest carbon density of Shaoguan city in 2017 was 43.176 tC/ha. The forest carbon density can be accurately estimated based on the species diversity index and stand spatial structure with machine learning algorithms. Therefore, a new method for the prediction of forest carbon density and carbon storage using species diversity indices and stand spatial structure can be explored. By analyzing the impacts of different biodiversity indices and stand spatial structure on forest carbon density, a scientific reference for the making of management measures for increasing forest carbon sinks and reducing emissions can be provided.

Carbon density in boreal forests responds non-linearly to temperature: An example from the Greater Khingan Mountains, northeast China

Boreal forests play a crucial role in the global carbon (C) cycle and in climate stabilization. To better predict global C budgets, it is important to accurately estimate the size of forest C pools, and to identify the factors affecting them. We used national forest inventory data for the Greater Khingan Mountains, northeast China from 1999 to 2018 and 149 additional field plots to estimate C storage and its changes in forest vegetation, excluding C stored in soils, and to calculate the total C density in forest ecosystems. From 1999 to 2018, the vegetation C storage and density increased by 92.22 Tg and 4.30 Mg C ha−1, respectively, while the mean C sink was 4.61 Tg C yr−1. Carbon storage and density showed the same pattern, with the largest stocks in trees, followed by herbs, shrubs, and then litter. Mean C density was higher in mature forests than in young forests. The maximum C density was recorded in Populus davidiana forests, and was 2.2-times larger than in Betula davurica forests (the minimum). The mean (± standard error) total C density of forest ecosystems was 111.3 ± 2.9 Mg C ha−1, including C stored in soils. Mean annual temperature (MAT) controlled total C density, as MAT had positive effects when it was lower than the temperature of the inflection point (-2.1 to -4.6 °C) and negative effects when it was above the inflection point. The rate of change in the total C density depended on the quantile points of the conditional distribution of total C density. Natural and anthropogenic disturbances had weaker effects on C density than temperature and precipitation. In conclusion, our results indicate that there might be a temperature-induced pervasive decrease in C storage and an increase in tree mortality across Eastern Asian boreal forests with future climate warming.

Green and easy synthesis of P-doped carbon-based hydrogen evolution reaction electrocatalysts

In this study, efficient electrodes for the hydrogen evolution reaction (HER) based on low-cost and metal-free carbon catalysts are presented. Phytic acid, a biosourced molecule containing carbon (C) and phosphorus (P), was found to be an excellent precursor for producing carbon materials with high P content, depending on the carbonization temperature, from 27.9 wt% at 700 °C to 7.3 wt% at 1000 °C. A green and easy route to produce P-doped carbon materials by heat treatment of this biosourced precursor without the need for additional reagents is thus proposed. We show that the conversion of P-O-type groups into P-C-type species is of paramount importance for improving the catalytic activity in HER of P-doped carbon materials. P-C-type groups appear to be the key factor in the electrocatalytic activity, reaching an onset potential of - 0.27 V. This study sheds light on the origin of the catalytic activity of P-doped carbons, in which P is expected to modify the homogenous distribution of the electron density of undoped carbons and increase their catalytic performance.