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Wetlands, as a crucial component of terrestrial ecosystems, play a significant role in global ecological services. Aboveground biomass (AGB) is a key indicator of the productivity and carbon sequestration potential of wetland ecosystems. The current research methods for remote-sensing estimation of biomass either rely on traditional vegetation indices or merely perform integer-order differential transformations on the spectra, failing to fully leverage the information complexity of hyperspectral data. To identify an effective method for estimating AGB of mixed-wetland-plant communities, we conducted field surveys of AGB from three typical wetlands within the Crested Ibis National Nature Reserve in Hanzhong, Shaanxi, and concurrently acquired canopy hyperspectral data with a portable spectrometer. The spectral features were transformed by applying fractional-order differentiation (0.0 to 2.0) to extract optimal feature combinations. AGB prediction models were built using three machine learning models, XGBoost, Random Forest (RF), and CatBoost, and the accuracy of each model was evaluated. The combination of fractional-order differentiation, vegetation indices, and feature importance effectively yielded the optimal feature combinations, and integrating vegetation indices with feature bands enhanced the predictive accuracy of the models. Among the three machine-learning models, the RF model achieved superior accuracy using the 0.8-order differential transformation of vegetation indices and feature bands (R2 = 0.673, RMSE = 23.196, RPD = 1.736). The optimal RF model was visually interpreted using Shapley Additive Explanations, which revealed that the contribution of each feature varied across individual sample predictions. Our study provides methodological and technical support for remote-sensing monitoring of wetland AGB. [ABSTRACT FROM AUTHOR]

In pursuit of sustainable solutions for water pollution mitigation, we have successfully employed electrospinning technology to fabricate a multilayered sodium alginate (SA)/polyethyleneimine (PEI)/polyvinyl alcohol (PVA) nanocomposite fiber membrane, with a focus on enhancing its adsorption capacity for Cu2+ ions in wastewater. Our research underscores the potential of this novel membrane, characterized by its small diameter, high uniformity, and expansive surface area, in effectively filtering heavy metal ions. By optimizing critical electrospinning parameters such as a voltage of 19.5 KV, a collector distance of 8 cm, a specific mass ratio of SA:PEI: PVA (1:2:6), and an injection rate of 8 μL/min, we achieved a nanofiber membrane with an average diameter of 112.5 nm, exhibiting exceptional morphological characteristics and high efficiency. Notably, the membrane exhibited an adsorption capacity of over 85% for Cu2+ during initial testing, maintaining over 80% efficiency throughout four consecutive filtration cycles. This work not only advances the field of nanocomposite membranes for water purification but also contributes significantly to the broader goal of achieving environmental sustainability by mitigating the impact of heavy metal contamination in water bodies. [ABSTRACT FROM AUTHOR]

Wetlands are ecologically and socioeconomically crucial areas. The application of economic valuation methods could ensure the sustainable utilization of wetlands. Utilizing wetland survey data from Jilin Province, China, representative of the years 2013 and 2017, we assessed the market value of water obtained from wetlands. Simultaneously, we employed the PLUS model to predict changes in wetland areas of different types over the next decade and analyzed their impact on the value of freshwater resource supplies. The results indicate the following points: (1) the area of wetlands decreased from 10,852.84 km2 in 2013 to 10,794.46 km2 in 2017 and that, in 2027, this was projected to further decrease to 10,614.37 km2, with river wetlands experiencing the most substantial decline; (2) the freshwater volumes in 2013 and 2017 were 20.81 × 108 and 20.09 × 108 m3, respectively, representing a 3.58% decrease. The volume for 2027 was projected to further reduce to 19.74 × 108 m3, with lake wetlands contributing the most to water resources and marsh wetlands contributing the least; and (3) the obtained total value of freshwater continuously increased, rising from CNY 8.384 billion in 2013 to CNY 8.642 billion in 2017, and this was projected to further increase to CNY 9.101 billion in 2027. There was regional variation in the value of wetland freshwater resource supplies, with differences in the per unit area and per capita value among administrative units. These findings can facilitate the optimal allocation of freshwater resources in Jilin Province, promoting its sustainable development while ensuring wetland conservation. [ABSTRACT FROM AUTHOR]

School bullying among primary and secondary school students has received increasing attention, and identifying relevant factors is a crucial way to reduce the risk of bullying victimization. Machine learning methods can help researchers predict and identify individual risk behaviors. Through a machine learning approach (i.e., the gradient boosting decision tree model, GBDT), the present longitudinal study aims to systematically examine individual, family, and school environment factors that can predict the risk of bullying victimization among primary and secondary school students a year later. A total of 2767 participants (2065 secondary school students, 702 primary school students, 55.20% female students, mean age at T1 was 12.22) completed measures of 24 predictors at the first wave, including individual factors (e.g., self-control, gender, grade), family factors (family cohesion, parental control, parenting style), peer factor (peer relationship), and school factors (teacher–student relationship, learning capacity). A year later (i.e., T2), they completed the Olweus Bullying Questionnaire. The GBDT model predicted whether primary and secondary school students would be exposed to school bullying after one year by training a series of base learners and outputting the importance ranking of predictors. The GBDT model performed well. The GBDT model yielded the top 6 predictors: teacher–student relationship, peer relationship, family cohesion, negative affect, anxiety, and denying parenting style. The protective factors (i.e., teacher–student relationship, peer relationship, and family cohesion) and risk factors (i.e., negative affect, anxiety, and denying parenting style) associated with the risk of bullying victimization a year later among primary and secondary school students are identified by using a machine learning approach. The GBDT model can be used as a tool to predict the future risk of bullying victimization for children and adolescents and to help improve the effectiveness of school bullying interventions. [ABSTRACT FROM AUTHOR]

Mangroves have important roles in regulating climate change, and in reducing the impact of wind and waves. Analysis of the chlorophyll content of mangroves is important for monitoring their health, and their conservation and management. Thus, this study aimed to apply four regression models, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Partial Least Squares (PLS) and Adaptive Boosting (AdaBoost), to study the inversion of Soil Plant Analysis Development (SPAD) values obtained from near-ground hyperspectral data of three dominant species, Bruguiera sexangula (Lour.) Poir. (B. sexangula), Ceriops tagal (Perr.) C. B. Rob. (C. tagal) and Rhizophora apiculata Blume (R. apiculata) in Qinglan Port Mangrove Nature Reserve. The accuracy of the model was evaluated using R2, RMSE, and MAE. The mean SPAD values of R. apiculata (SPADavg = 66.57), with a smaller dispersion (coefficient of variation of 6.59%), were higher than those of C. tagal (SPADavg = 61.56) and B. sexangula (SPADavg = 58.60). The first-order differential transformation of the spectral data improved the accuracy of the prediction model; R2 was mostly distributed in the interval of 0.4 to 0.8. The accuracy of the XGBoost model was less affected by species differences with the best stability, with RMSE at approximately 3.5 and MAE at approximately 2.85. This study provides a technical reference for large-scale detection and management of mangroves. [ABSTRACT FROM AUTHOR]

Hyperspectral inversion techniques can facilitate soil quality monitoring and evaluation. In this study, the Yellow River Delta Wetland Nature Reserve was used as the study area. By measuring and analyzing soil samples under different vegetation types and collecting soil reflectance spectra, the relationships between vegetation types, soil depth, and the changes in soil total carbon (TC), total nitrogen (TN), and total phosphorus (TP) contents were assessed. The spectral data set was changed by spectral first derivative processing and division of the sample set according to vegetation type. The correlation between soil carbon, nitrogen, and phosphorus contents, and soil spectra was also analyzed, sensitive bands were selected, and the partial least-squares (PLS) method, support vector machine (SVM) method, and random forest (RF) model were used to establish the inversion model based on the characteristic bands. The optimal combination of spectral transformation, sample set partitioning, and inversion model was explored. The results showed significant differences (p < 0.05) in soil TC, TN, and TP contents under reed and saline alkali poncho vegetation, but not between soil element contents under different stratifications of the same plant species. The first derivative reflectance had higher correlation coefficients with soil TC, TN, and TP contents compared with the original reflectance, while the sensitive bands and quantities of the three elements differed. The division of the sample sets according to vegetation type and the first derivative treatment can improve the prediction accuracy of the model. The best combination of sample set plus FD plus RF for TC, TN, and TP in reed soil and sample set plus FD plus SVM for TC, TN, and TP in saline alkali pine soil provides technical support to further improve the prediction accuracy of TC, TN, and TP in wetland soil. [ABSTRACT FROM AUTHOR]

Focusing on the family system, this study simultaneously examined the effects of the parental factor, family functioning, and individual factor on youth psychological well-being. Overall, 332 youths and their parents were involved in this research and responded to an online questionnaire measuring parental optimism, family cohesion, youth optimism, and youth psychological well-being. The results suggested that (1) parental optimism was positively related to youth psychological well-being; (2) both family cohesion and youth optimism mediated the connection between parental optimism and youth psychological well-being; and (3) the link between parental optimism and youth psychological well-being was mediated by family cohesion and youth optimism in sequence. The present study reveals the underlying mechanism of how to improve youth psychological well-being from within the family system. [ABSTRACT FROM AUTHOR]

Nutrient removal in tidal flow constructed wetlands (TF-CW) is a complex series of nonlinear multi-parameter interactions. We simulated three tidal flow systems, a continuous vertical flow system filled with synthetic wastewater, compared the influent, effluent concentrations to examine (1) nutrient removal in artificial TF-CWs,, (2) the ability of a backpropagation (BP) artificial neural network to predict nutrient removal. The nutrient removal rates were higher under tidal flow when the idle/reaction time was two,, reached 90 ± 3%, 99 ± 1%,, 58 ± 13% for total nitrogen (TN), ammonium nitrogen (NH4+-N),, total phosphorus (TP), respectively. The main influences on nutrient removal for each scenario were identified by redundancy analysis, were input into the model to train, verify the pollutant effluent concentrations. Comparison of the actual, model-predicted effluent concentrations showed that the model predictions were good. The predicted, actual values were correlated, the margin of error was small. The BP neural network fitted best to TP, with an R2 of 0.90. The R2 values of TN, NH4+-N,, nitrate nitrogen (NO3−-N) were 0.67, 0.73,, 0.69, respectively. [ABSTRACT FROM AUTHOR]

Carbon, nitrogen, and phosphorus—nutrient and restrictive elements for plant growth and important components of the plant body—are mainly transferred and exchanged between plants and the soil environment. Changes in the carbon, nitrogen, and phosphorus eco-stoichiometry greatly impact the growth and expansion of Spartina alterniflora, and understanding these changes can reveal the nutrient coordination mechanism among ecosystem components. To explore the relationship between leaf and soil eco-stoichiometry and determine the key soil factors that affect leaf eco-stoichiometry, we collected leaf and soil samples of S. alterniflora at different tidal levels (i.e., 1, 3, and 5 km away from the coastline) in a coastal wetland in the Yancheng Elk Nature Reserve, Jiangsu province. We measured the leaf and soil carbon, nitrogen, and phosphorus contents and ratios, as well as the soil salinity and soil organic carbon. The results revealed the following. (1) The leaf stoichiometric characteristics and soil properties of S. alterniflora differed significantly between tidal levels; for example, total carbon, nitrogen, soil organic carbon were detected at their highest levels at 3 km and lowest levels at 5 km. (2) Significant correlations were detected between the leaf stoichiometric characteristics and soil characteristics. Additionally, nitrogen limitation was evident in the study area, as indicated by the nitrogen–phosphorus ratio being less than 14 and the soil nitrogen–phosphorus ratio being less than 1. (3) Soil salinity and the soil carbon–nitrogen ratio were shown to be the key factors that affect the eco-stoichiometric characteristics of S. alterniflora. These findings furthered our understanding of the nutrient distribution mechanisms and invasion strategy of S. alterniflora and can thus be used to guide S. alterniflora control policies formulated by government management departments in China. [ABSTRACT FROM AUTHOR]

Decomposition of emergent macrophytes is now recognized as an internal nutrient source for shallow lakes. Temperate lakes always experience seasonal ice cover in winter, but the influences of emergent macrophytes decomposition on water quality have rarely been examined under ice. Here, we conducted an incubation experiment to investigate winter decomposition of two common emergent macrophytes species (Typha orientalis and Phragmites australis) and its influences on water quality in the Hengshui Lake, North China. Mesocosms simulating a lake ice regime were incubated in the field for 120 days in winter and were treated with and without plant material addition. Water quality was monitored through dissolved oxygen (DO), dissolved organic carbon (DOC), total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH4-N), and nitrate nitrogen (NO3-N). We found that both species were significantly decomposed in winter and that the majority of mass loss occurred in the first 10 days of decomposition when the water surface of mesocosms were already frozen. The concentrations of DO rapidly dropped to values close to zero after plant material submergence. At the end of incubation, the concentrations of DOC, TN, and NO3-N in the mesocosms with plant material addition were significantly higher than initial concentrations. In contrast, the concentrations of DOC, TN, TP, NO3-N, and NH4-N in the mesocosms without plant material addition were equal to or less than initial concentrations. Our research suggests that winter decomposition of emergent macrophytes produces negative influences on water quality under ice that lasts for the whole winter. [ABSTRACT FROM AUTHOR]

Studying the stoichiometric characteristics of plant C, N, and P is an effective way of understanding plant survival and adaptation strategies. In this study, 60 fixed plots and 120 random plots were set up in a reed-swamp wetland, and the canopy spectral data were collected in order to analyze the stoichiometric characteristics of C, N, and P across all four seasons. Three machine models (random forest, RF; support vector machine, SVM; and back propagation neural network, BPNN) were used to study the stoichiometric characteristics of these elements via hyperspectral inversion. The results showed significant differences in these characteristics across seasons. The RF model had the highest prediction accuracy concerning the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.88, 0.95, 0.97, and 0.92, respectively. According to the root mean square error (RMSE) results, the model error of total C (TC) inversion is the smallest, and that of C/N inversion is the largest. The SVM yielded poor predictive results for the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.82, 0.81, 0.81, and 0.70, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The BPNN yielded high stoichiometric prediction accuracy. The R2 of the four-season models was greater than 0.87, 0.96, 0.84, and 0.90, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The accuracy and stability of the results were verified by comprehensive analysis. The RF model showed the greatest prediction stability, followed by the BPNN and then the SVM models. The results indicate that the accuracy and stability of the RF model were the highest. Hyperspectral data can be used to accurately invert the stoichiometric characteristics of C, N, and P in wetland plants. It provides a scientific basis for the long-term dynamic monitoring of plant stoichiometry through hyperspectral data in the future. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) is an essential source of atmospheric pollution in metropolitan areas since it has adverse effects on human health. However, previous research suggested wetlands can remove particulate matter from the atmosphere to land surfaces. This study was conducted in the Hanshiqiao Wetland National Nature Reserve in Beijing during 2016. The concentrations of PM10 and PM2.5 on a wetland and bare land in the park, as well as metrological data, were collected during the whole year. Based on the observed data, removal efficiency of each land use type was calculated by empirical models and the relationships between concentrations and metrological factors were also analyzed. The results indicated that: (1) In general, the PM10 and PM2.5 concentrations on the bare land surface were higher than those on the wetland surface, in both of which the highest value appeared at night and evening, while the lowest value appeared near noon. In terms of season, the average concentration of PM10 was higher in winter (wetland: 137.48 μg·m−3; bare land: 164.75 μg·m−3) and spring (wetland: 205.18 μg·m−3; bare land: 244.85 μg·m−3) in general. The concentration of PM2.5 on the wetland surface showed the same pattern, while that on the bare land surface was higher in spring and summer. (2) Concentrations of PM10 and PM2.5 were significantly correlated with the relative humidity (p < 0.01) and inversely correlated with wind speed (p < 0.05). The relationship between PM10 and PM2.5 concentrations and temperature was more complicated—it showed a significantly negative correlation (p < 0.01) between them in winter and spring, however, the correlation was insignificant in autumn. In summer, only the correlation between PM10 concentration and temperature on the wetland surface was significant (p < 0.01). (3) The dry removal efficiency of PM10 was greater than that of PM2.5. The dry removal efficiencies of PM10 and PM2.5 followed the order of spring > winter > autumn > summer on the wetland. This study seeks to provide practical measures to improve air quality and facilitate sustainable development in Beijing. [ABSTRACT FROM AUTHOR]

Potential changes in both the intensity and frequency of extreme drought events are vital aspects of regional climate change that can alter the distribution and dynamics of water availability and subsequently affect carbon cycles at the ecosystem level. The effects of extreme drought events on the carbon budget of peatland in the Zoige plateau and its response mechanisms were studied using an in-field controlled experimental method. The results indicated that the peatland ecosystem of the Zoige plateau functioned as a carbon sink while under the control (CK) or extreme drought (D) treatment throughout the entire growing season. Maximum fluxes of methane (CH4) emissions and the weakest carbon sink activity from this ecosystem were in the early growth stage, the most powerful carbon sink activity was during the peak growth stage, while the absorption sink activity of carbon dioxide (CO2) and CH4 was present during the senescence stage. Extreme drought reduced the gross primary productivity (GPP) and ecosystem respiration (Re) of the peatland ecosystem by 14.5% and 12.6%, respectively (p < 0.05) and the net ability to store carbon was reduced by 11.3%. Overall, the GPP was highly sensitive to extreme drought. Moreover, extreme drought significantly reduced the CH4 fluxes of the ecosystem and even changed the peatland from a CH4 emission source to a CH4 sink. Subsequent to drought treatment, extreme drought was also shown to have a carry-over effect on the carbon budget of this ecosystem. Soil water content and soil temperature were the main driving factors of carbon budget change in the peatland of the Zoige plateau, but with the increase in soil depth, these driving forces were decreased. The findings indicated that frequent extreme drought events in the future might reduce the net carbon sink function of peatland areas, with an especially strong influence on CO2. [ABSTRACT FROM AUTHOR]

Environmental abiotic stresses are limiting factors for less tolerant organisms, including soil plants. Abiotic stress tolerance-associated genes from prokaryotic organisms are supposed to have a bright prospect for transgenic application. The drought-adapted cyanobacterium Nostoc flagelliforme is arising as a valuable prokaryotic biotic resource for gene excavation. In this study, we evaluated the salt-tolerant function and application potential of a candidate gene drnf1 from N. flagelliforme, which contains a P-loop NTPase (nucleoside-triphosphatase) domain, through heterologous expression in two model organisms Synechocystis sp. PCC 6803 and Arabidopsis thaliana. It was found that DRNF1 could confer significant salt tolerance in both transgenic organisms. In salt-stressed transgenic Synechocystis, DRNF1 could enhance the respiration rate; slow-down the accumulation of exopolysaccharides; up-regulate the expression of salt tolerance-related genes at a higher level, such as those related to glucosylglycerol synthesis, Na+/H+ antiport, and sugar metabolism; and maintain a better K+/Na+ homeostasis, as compared to the wild-type strain. These results imply that DRNF1 could facilitate salt tolerance by affecting the respiration metabolism and indirectly regulating the expression of important salt-tolerant genes. Arabidopsis was employed to evaluate the salt tolerance-conferring potential of DRNF1 in plants. The results show that it could enhance the seed germination and shoot growth of transgenic plants under saline conditions. In general, a novel prokaryotic salt-tolerant gene from N. flagelliforme was identified and characterized in this study, enriching the candidate gene pool for genetic engineering in plants. [ABSTRACT FROM AUTHOR]

Peatlands play an essential role in the global carbon (C) and nitrogen (N) cycling. In order to ascertain the draining effects on recent accumulation rates of C (RERCA) and N (RERNA) in the Zoige peatland in the eastern Qinghai-Tibet Plateau, the core samples of peat growth, C and N accumulation for both natural and drained peatlands were measured using 210Pb and 137Cs dating methods. As a result, RERCA and RERNA showed an increasing trend from the bottom to the surface of the peatland, which was in accordance with the peat accumulation rates. However, the average RERCA in permanently flooded and seasonally flooded peatlands were 1.5–2.5 times that of drainage peatlands, and the average of RERNA were 1.2–1.7 times. Our findings indicate that the Zoige peatland is still in the stage of peat development with a large carbon sequestration capacity, and drainage from human activities leads to the decreasing of RERCA and RERNA, which will contribute to the selection of the effective ways to slow down the anthropogenic effects on the degradation of the Zoige peatland. [ABSTRACT FROM AUTHOR]

Wetlands are one of the world’s most productive ecosystems, and therefore it is crucial that management decisions regarding wetlands incorporate awareness of accurate assessments of the value of their respective ecosystem services. In this paper, we seek to improve the modelling precision in the scale transform process of ecosystem service evaluation. Firstly, we selected eight services as the criteria to calculate wetland ecosystem values: substance production, flood control, carbon sequestration, gas regulation, climate regulation, wave reduction, adding new lands, recreation and education. Then, six coastal wetlands of Liaoning province were chosen as the case study areas, and their ecosystem values were calculated by empirical method. Next, we simulated ecosystem values of the six cases by two spatial-scales transform methods named meta-analysis and wavelet transform. Finally, we compared the two groups of simulated values with the empirical measured values to examine their evaluation precisions. The results indicated that the total precision of the wavelet transform model (0.968) was higher than that of meta-analysis (0.712). In addition, the simulated values of single services such as substance production, flood control, carbon sequestration, gas regulation, and climate regulation were closer to the measured values using wavelet transform model. This research contributes to identifying an evaluation model with higher precision for evaluating wetland ecosystem services in the process of scale transform. [ABSTRACT FROM AUTHOR]

The chlorophyll content can indicate the general health of vegetation, and can be estimated from hyperspectral data. The aim of this study is to estimate the chlorophyll content of mangroves at different stages of restoration in a coastal wetland in Quanzhou, China, using proximal hyperspectral remote sensing techniques. We determine the hyperspectral reflectance of leaves from two mangrove species, Kandelia candel and Aegiceras corniculatum, from short-term and long-term restoration areas with a portable spectroradiometer. We also measure the leaf chlorophyll content (SPAD value). We use partial-least-squares stepwise regression to determine the relationships between the spectral reflectance and the chlorophyll content of the leaves, and establish two models, a full-wave-band spectrum model and a red-edge position regression model, to estimate the chlorophyll content of the mangroves. The coefficients of determination for the red-edge position model and the full-wave-band model exceed 0.72 and 0.82, respectively. The inverted chlorophyll contents are estimated more accurately for the long-term restoration mangroves than for the short-term restoration mangroves. Our results indicate that hyperspectral data can be used to estimate the chlorophyll content of mangroves at different stages of restoration, and could possibly be adapted to estimate biochemical constituents in leaves. [ABSTRACT FROM AUTHOR]