Permafrost, a major component of the cryosphere, is undergoing rapid degradation due to climate change, human activities, and other external disturbances, profoundly impacting ecosystems, hydroclimate, engineering geological stability, and infrastructure. In Northeast China, the thermal dynamics of Xing'an permafrost (XAP) are particularly complex, complicating the accurate assessment of its spatial extent. Many earlier mapping efforts, despite significant progress, fall short in accounting for some key local geo-environmental factors. Thus, this study introduces a new approach that incorporates four key driving factors-biotic, climatic, physiographic, and anthropogenic-by integrating multisource datasets and in situ observations. Four machine learning (ML) models [random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGB)] are applied to simulate permafrost distribution and probability, as well as to evaluate their performance. The results indicate that models' accuracy, ranked from highest to lowest, is as follows: RF (area under the curve (AUC) =0.88 and accuracy =0.81), XGB (0.86 and 0.77), LR (0.81 and 0.73), and SVM (0.76 and 0.66), with RF emerging as the most effective model for permafrost mapping in Northeast China. Analysis of the relationships between predictors and permafrost occurrence probability (POP) indicates that vegetation and snow cover exert nonlinear effects on permafrost, while human activities significantly reduce POP. Additionally, finer soil textures and higher soil organic matter content are positively correlated with increased POP. The modeling results, combined with field survey data, also show that permafrost is more prevalent in lowlands than in uplands, confirming the symbiotic relationship between permafrost and wetlands in Northeast China. This spatial variation is influenced by local microclimates, runoff patterns, and soil thermal properties. The primary sources of model error are uncertainties in the accuracy of multisource datasets at different scales and the reliability of observational data. Overall, ML models demonstrate great potential for mapping permafrost in Northeast China.

Coastal wetlands are extremely vulnerable to both marine damage and human activities. In order to protect these wetlands, many artificial seawalls have been constructed. However, studies are required to understand how coastal wetlands will evolve under the influence of artificial seawalls. Therefore, to understand this succession process of plants and their adaptation to habitats divided by seawalls, two different habitats inside and outside the seawalls were selected in Laizhou Bay, China. The results showed that there were 5 plant species outside the seawalls that were lower than the 13 species inside. Additionally, the dominant plant species were varied between the two habitats, with mostly annual herbs observed outside the seawalls and perennial shrubs inside. Soil salinity was higher outside the seawalls, which was the key impact factor of soil nutrient differences. The distribution of annual and perennial species may be constrained by spatial differences in soil stoichiometry. Therefore, the plants in coastal wetlands vary significantly at a small scale in response to the disturbance of artificial seawalls. The differences in soil and plants between the two habitats divided by the artificial seawalls provide a new insight for evaluating the artificial coastal projects. The only way to reduce the effects of seawalls on natural coastal wetland vegetation and ecosystem functions is to restore connectivity of tidal flow inside and outside the seawalls.

Petroleum pollution in soil is very damaging to the areas affected by the accidental release of petroleum hydrocarbons and has destructive impacts on natural resources and environmental health. Therefore, its monitoring and analysis are critical, however, due to the cost and time associated with chemical approaches, finding a quick and cost-effective analytical method is valuable. This study was conducted to evaluate the potential of using visible near infrared (Vis-NIR) spectroscopy to predict total petroleum hydrocarbons (TPH) in polluted soils around the Shadegan ponds, in southern Iran. One hundred soil samples showing various degrees of pollution were randomly collected from topsoil (0-10 cm). The soil samples were analyzed for TPH using Vis-NIR reflectance spectroscopy in the laboratory and then following application of preprocessing transformation, partial least squares PLS regression as well as two machine learning models including random forest (RF), and support vector machine (SVM) were examined. The results showed that the reflectance values at 1725 nm and 2311 nm, respectively, served as indicative TPH reflectance features, exhibiting weaker reflection with rising TPH. Among the preprocessing methods, the baseline correction method indicated the highest performance than the others. According to the evaluation model criteria in the validation dataset, the efficiency of the three selected models was observed in the following order SVM > RF > PLS regression. The SVM model provided the best performance in the validation dataset with r(2) = 0.85, root mean of square (RMSEP = 1.59 %, and the ratio of prediction to deviation (RPD) = 2.6. Overall, this study provided strong evidence supporting the considerable potential of Visible-NIR spectroscopy as a rapid and cost-effective technique for estimating TPH levels in oil-contaminated soils, surpassing traditional chemical analytical methods. Applying the mid-infrared spectrum (MIR) in combination with Visible-NIR data is expected to provide more comprehensive and accurate results when assessing soils in polluted areas, thereby enhancing the accuracy and reliability of the results across a diverse range of soil types.

Wetland area and condition are declining globally despite their importance to climate change mitigation and biodiversity. Introduced ungulate species are contributing to the global decline. Their impacts on wetlands are widespread and varied, however poorly understood. We summarise global impacts of introduced unmanaged and domesticated ungulates on wetlands highlighting potential outcomes of their removal. We place an emphasis on Australia due to the disproportionate impacts of ungulates on wetlands and potential for emerging carbon and biodiversity markets to incentivise private investment in wetland conservation and restoration. Our Systematic Literature Review assessed impacts of cattle, pigs, horses, deer, buffalo, sheep, camels, and other ungulates on wetlands. There were 372 relevant resources from 35 countries, with highest representation from Australia and the United States. The majority related to cattle (29 %) and pigs (19 %). More impacts were reported in freshwater wetlands (51 %) than marine (19 %). A quarter of studies related to riparian habitats. Ungulate impacts varied geographically and among climates. More studies reported soil damage, weed dispersal, decreased vegetation cover, and woody vegetation suppression than neutral or positive changes in these metrics. Decreases in richness and abundance of native flora and fauna were more frequently reported than increases. Of 33 studies reporting wetland carbon impacts, 24 reported increased CO2 emissions due to loss of soil carbon or vegetation biomass. Ungulate exclusion from wetlands could enhance carbon stocks and biodiversity, however further studies comparing wetland typologies and carbon dynamics are needed to quantify levels of enhancement given differences in ungulate species and environments.

Constructed wetlands (CWs) have been widely used for treating polluted water since the 1950s, with applications in over 50 countries worldwide. Most studies investigating the pollutant removal efficiency of these wetlands have focused on differences among wetland designs, operation strategies, and environmental conditions. However, there still remains a gap in understanding the variation in wetland pollutant removal efficiency over different time scales. Therefore, the main aim of the study is to address this gap by conducting a global metaanalysis to estimate the variation in nitrogen (N) and phosphorus (P) removal by wetland in short- and longterm pollutant treatment. The findings of this study indicated that the total efficiencies of N and P removal increased during short-term wetland operation but decreased during long-term operation. However, for surface flow CWs specifically, the efficiencies of N and P removal increased during short-term operation and remained stable during long-term operation. Moreover, the study discovered that wetland N removal efficiency was influenced by seasons, with an increase in spring and summer and a decrease in autumn and winter. Conversely, there was no significant seasonal effect on P removal efficiency. Additionally, high hydraulic load impaired wetland N and P removal efficiency during long-term operation. This study offers a critical review of the role of wetlands in wastewater treatment and provides valuable reference data for the design and selection of CWs types during wastewater treatment in the aspect of sustainability.

Glyphosate is a foliar herbicide detected in soil, sediment, and water, causing non -visible damage to non -target organisms, potentially affecting the diversity, structure, and functioning of microbial communities associated with riparian vegetation that provide ecosystem services. The objective of the present work was 1) to determine the viable counts of microorganisms and 2) to analyze how the enzymatic activities associated with the metabolism of carbon, phosphorus, and nitrogen are affected in the riparian plants' rhizosphere ( Fimbristylis dichotoma , Ludwigia octovalvis , and Typha domingensis ) exposed to glyphosate. The plants were collected with the same soil in which they lived to maintain the micro -habitat of the rhizosphere. Zero or fifty mg of glyphosate acid equivalent (ae)/L was applied to the plants at the ground level for 15 days. Actinomycetes, total bacteria (including actinomycetes), and fungi were then isolated and quantified, and the activity of 19 enzymes (metabolism of P, C, and N) were analyzed from rhizosphere samples. In the presence of the herbicide, it was found that 1) bacteria was most negatively affected compared to actinomycetes and fungi, and 2) microbial populations isolated from L. octovalvis were lesser than those from T. domingensis and F. dichotoma . Rhizosphere enzymatic activities showed that phosphorus and carbon metabolism were stimulated by glyphosate. The information obtained from this work allows us to identify the response of cultivable microbial diversity and functional diversity of the rhizosphere of ecologically important plants.

Freezing and thawing profoundly affect soil carbon cycling. Under the influence of climate change, rising temperatures and glacier shrinkage in arid regions have increased the spring river supply to lakes. However, intense evaporation in summer and seasonal fluctuations in lake water levels alter the magnitude and direction of carbon emissions. Yet, the mechanisms of temperature and groundwater level factors on arid zone lake wetlands remain unclear. This study, through field monitoring, found that during soil freezing periods, Phragmites reduced emissions by 95.21% and increased emissions by 3.91% during thawing periods. Tamarix Chinensis and bare land exhibited a decrease in carbon uptake of 42.77% and 85.25% during soil freezing periods, and a decrease in carbon uptake of 41.98% and 2.17% during thawing periods. By constructing a freeze-thaw simulation device, we simulated CO2 emissions characteristics under different water level conditions during freeze-thaw processes, including water injection at 10 cm, 20 cm, 30 cm, 40 cm (corresponding to water levels 40 cm, 30 cm, 20 cm, 10 cm below the soil surface), as well as scenarios of anhydrous and flooding periods. The results showed that under freeze-thaw conditions, Phragmites exhibited the strongest carbon uptake when water was injected at 20 cm, transitioning from emissions during the anhydrous period to carbon uptake. Tamarix Chinensis exhibited the strongest carbon uptake during freeze-thaw cycles when water was injected at 10 cm, showing a 93.69% increase compared to the anhydrous period. Meanwhile, the bare land exhibited the strongest carbon uptake during freeze-thaw cycles in the no water period. Lower temperatures and higher water levels favor increased carbon uptake in lake wetlands. This study identifies optimal water levels for carbon uptake in lake wetlands during freeze-thaw, and the important role of water level and temperature conditions on carbon emissions, providing valuable insights for assessing the carbon feedback mechanisms in lake wetlands under future climate change.

Tropical high-Andean wetlands, locally known as 'bofedales', are key ecosystems sustaining biodiversity, carbon sequestration, water provision and livestock farming. Bofedales' contribution to dry season baseflows and sustaining water quality is crucial for downstream water security. The sensitivity of bofedales to climatic and anthropogenic disturbances is therefore of growing concern for watershed management. This study aims to understand seasonal water storage and release characteristics of bofedales by combining remote sensing analysis and ground-based monitoring for the wet and dry seasons of late 2019 to early 2021, using the glacierised Vilcanota-Urubamba basin (Southern Peru) as a case study. A network of five ultrasound loggers was installed to obtain discharge and water table data from bofedal sites across two headwater catchments. The seasonal extent of bofedales was mapped by applying a supervised machine learning model using Random Forest on imagery from Sentinel-2 and NASADEM. We identified high seasonal variability in bofedal area with a total of 3.5% and 10.6% of each catchment area, respectively, at the end of the dry season (2020), which increased to 15.1% and 16.9%, respectively, at the end of the following wet season (2021). The hydrological observations and bofedal maps were combined into a hydrological conceptual model to estimate the storage and release characteristics of the bofedales, and their contribution to runoff at the catchment scale. Estimated lag times between 1 and 32 days indicate a prolonged bofedal flow contribution throughout the dry season (about 74% of total flow). Thus, our results suggest that bofedales provide substantial contribution to dry season baseflow, water flow regulation and storage. These findings highlight the importance of including bofedales in local water management strategies and adaptation interventions including nature-based solutions that seek to support long-term water security in seasonally dry and rapidly changing Andean catchments.

Purpose Warming-induced permafrost degradation is anticipated to change the global carbon cycle. We attempted to determine the effect of permafrost degradation on carbon emissions and carbon sequestration of seven wetlands in three zones of Northeast China, aiming to investigate the responses of carbon sources/sinks to permafrost degradation. Methods Three zones (permafrost zone, PZ; discontinuous permafrost zone, DPZ; and permafrost degradation zone, PDZ) were selected to represent permafrost degradation stages. In each zone, we selected seven wetlands along the moisture gradient, namely, marsh (M), thicket swamp (TS), forested swamps (alder swamp, FAS; birch swamp, FBS; and larch swamp, FLS), forested fen (larch fen, FLF), and forested bog (larch bog, FLB). We determined the annual carbon emissions of soil heterotrophic respiration from seven wetlands and the annual net carbon sequestration of vegetation, evaluated the net carbon balance by calculating the difference between annual net carbon sequestration and annual carbon emissions, and then determined the magnitude and direction of carbon-climate feedback. Results and discussion With permafrost degradation, most forested wetlands (excluding FAS in PDZ) still acted as carbon sinks in DPZ (0.30 - 1.88 t ha(-1) year(-1)) and PDZ (0.31 - 1.76 t ha(-1) year(-1)) in comparison to PZ (0.46 - 2.43 t ha(-1) year(-1)). In contrast, M and TS acted as carbon sources in DPZ (-1.72 and -0.82 t ha(-1) year(-1)) and PDZ (-2.66 and -0.98 t ha(-1) year(-1)) in comparison to PZ (-0.86 and 0.03 t ha(-1) year(-1)), this result could be attributed to the increased CO2 emissions (promoted by warmer soil temperatures) and CH4 emissions (promoted by warmer soil temperatures, higher water tables and greater thaw depths), the two significantly increased the annual carbon emissions (increased by 8.8 - 14.4% in DPZ and by 35.0 - 46.0% in PDZ), and the annual carbon emissions > the annual net carbon sequestration. Furthermore, in terms of net radiative forcing, five forested wetlands still showed negative net radiative forcing in DPZ (-6.90 to -1.10 t CO2-eq ha(-1) year(-1)) in comparison to PZ (-8.91 to -1.62 t CO2-eq ha(-1) year(-1)). In contrast, in PDZ, only FLB showed negative net radiative forcing (-6.29 t CO2-eq ha(-1) year(-1)) and significantly increased by 288.3% compared to PZ (P < 0.05), indicating an ever-increasing net cooling impact, while the other four forested wetlands all turned into positive net radiative forcing (0.84 - 53.56 t CO2-eq ha(-1) year(-1)) because of higher CH4 (CO2-eq) emissions, indicating net warming impacts. Conclusions Our results indicated that permafrost degradation affected the carbon sources/sinks of seven wetlands via different mechanisms. M and TS acted as carbon sources in both DPZ and PDZ, while permafrost degradation did not change the overall direction of the net carbon balance of five forested wetlands. Most forested wetlands (excluding FAS in PDZ) still acted as carbon sinks in both DPZ and PDZ, although there were fluctuations in carbon sink values. Moreover, despite being carbon sinks, most forested wetlands (excluding FLB) in PDZ showed positive net radiative forcing compared to DPZ and PZ (negative net radiative forcing) when using the methodology of CO2 equivalent, indicating climatic warming impacts, while FLB showed negative net radiative forcing, indicating a climatic cooling impact. Therefore, FLB should be protected as a priority in the subsequent carbon sink management practices in permafrost zones.

How methane (CH4) fluxes from alpine peatlands, especially during freeze-thaw cycles, affect the global CH4 budget is poorly understood. The present research combined the eddy covariance method, incubation experiments and high-throughput sequencing to observe CH4 flux from an alpine fen during thawing-freezing periods over a period of four years. The response of CH4 production potential and methanogenic archaea to climate change was analyzed. We found a relatively high mean annual cumulative CH4 emission of 37.7 g CH4-C m(-2). The dominant contributor to CH4 emission was the thawing period: warmer, longer thawing periods contributed 69.1-88.6% to the annual CH4 budget. Non-thawing periods also contributed, with shorter frozen-thawing periods accounting for up to 18.5% and shorter thawing-freezing periods accounting for up to 8.8%. Over the course of a year, emission peaked in the peak growing season and at onset of thawing and freezing. In contrast, emission did not vary substantially during the frozen period. Daily mean emission was highest during the thawing period and lowest during the frozen period. Diurnal patterns of CH4 emission were similar among the four periods, with peaks ranging from 12:00 to 18:00 and the lowest emission around 00:00. Water table and temperature were the dominant factors controlling CH4 emissions during different thawing-freezing periods. Our results suggest that CH4 emission from peatland will change substantially as CH4 production, microbial composition, and patterns of thawing-freezing cycles change with global warming. Therefore, frequent monitoring of CH4 fluxes in more peatlands and in situ monitoring of methanogenesis and related microbes are needed to provide a clear picture of CH4 fluxes and the thawing-freezing processes that affect them.