Patterns and drivers of atmospheric nitrogen deposition retention in global forests.

Forests are the largest carbon sink in terrestrial ecosystems, and the impact of nitrogen (N) deposition on this carbon sink depends on the fate of external N inputs. However, the patterns and driving factors of N retention in different forest compartments remain elusive. In this study, we synthesized 408 observations from global forest 15N tracer experiments to reveal the variation and underlying mechanisms of 15N retention in plants and soils. The results showed that the average total ecosystem 15N retention in global forests was 63.04 ± 1.23%, with the soil pool being the main N sink (45.76 ± 1.29%). Plants absorbed 17.28 ± 0.83% of 15N, with more allocated to leaves (5.83 ± 0.63%) and roots (5.84 ± 0.44%). In subtropical and tropical forests, 15N was mainly absorbed by plants and mineral soils, while the organic soil layer in temperate forests retained more 15N. Additionally, forests retained more N 15 H 4 + $$ {}^{15}\mathrm{N}{\mathrm{H}}_4^{+} $$ than N 15 O 3 - $$ {}^{15}\mathrm{N}{\mathrm{O}}_3^{-} $$ , primarily due to the stronger capacity of the organic soil layer to retain N 15 H 4 + $$ {}^{15}\mathrm{N}{\mathrm{H}}_4^{+} $$ . The mechanisms of 15N retention varied among ecosystem compartments, with total ecosystem 15N retention affected by N deposition. Plant 15N retention was influenced by vegetative and microbial nutrient demands, while soil 15N retention was regulated by climate factors and soil nutrient supply. Overall, this study emphasizes the importance of climate and nutrient supply and demand in regulating forest N retention and provides data to further explore the impacts of N deposition on forest carbon sequestration.

Perturbation diversity certificates robust generalization.

Whilst adversarial training has been proven to be one most effective defending method against adversarial attacks for deep neural networks, it suffers from over-fitting on training adversarial data and thus may not guarantee the robust generalization. This may result from the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way so that the resulting adversarial examples are highly biased towards the decision boundary, leading to an inhomogeneous data distribution. To mitigate this limitation, we propose to generate adversarial examples from a perturbation diversity perspective. Specifically, the generated perturbed samples are not only adversarial but also diverse so as to certify robust generalization and significant robustness improvement through a homogeneous data distribution. We provide theoretical and empirical analysis, establishing a foundation to support the proposed method. As a major contribution, we prove that promoting perturbations diversity can lead to a better robust generalization bound. To verify our methods' effectiveness, we conduct extensive experiments over different datasets (e.g., CIFAR-10, CIFAR-100, SVHN) with different adversarial attacks (e.g., PGD, CW). Experimental results show that our method outperforms other state-of-the-art (e.g., PGD and Feature Scattering) in robust generalization performance.

Multi-elemental stoichiometric ratios of atmospheric wet deposition in Chinese terrestrial ecosystems.

Intense human activities have significantly altered the concentrations of atmospheric components that enter ecosystems through wet and dry deposition, thereby affecting elemental cycles. However, atmospheric wet deposition multi-elemental stoichiometric ratios are poorly understood, hindering systematic exploration of atmospheric deposition effects on ecosystems. Monthly precipitation concentrations of six elements-nitrogen (N), phosphorus (P), sulfur (S), potassium (K), calcium (Ca), and magnesium (Mg)-were measured from 2013 to 2021 by the China Wet Deposition Observation Network (ChinaWD). The multi-elemental stoichiometric ratio of atmospheric wet deposition in Chinese terrestrial ecosystems was N: K: Ca: Mg: S: P = 31: 11: 67: 5.5: 28: 1, and there were differences between vegetation zones. Wet deposition N: S and N: Ca ratios exhibited initially increasing then decreasing inter-annual trends, whereas N: P ratios did not exhibit significant trends, with strong interannual variability. Wet deposition of multi-elements was significantly spatially negatively correlated with soil nutrient elements content (except for N), which indicates that wet deposition could facilitate soil nutrient replenishment, especially for nutrient-poor areas. Wet N deposition and N: P ratios were spatially negatively correlated with ecosystem and soil P densities. Meanwhile, wet deposition N: P ratios were all higher than those of ecosystem components (vegetation, soil, litter, and microorganisms) in different vegetation zones. High input of N deposition may reinforce P limitations in part of the ecosystem. The findings of this study establish a foundation for designing multi-elemental control experiments and exploring the ecological effects of atmospheric deposition.

Evaluation of adiponectin and TNF-α expression in diabetic patients and its relationship with cardiovascular diseases.

Diabetes is caused by peripheral insulin resistance and lack of insulin secretion due to the apoptosis of pancreatic beta cells. Tumor necrosis factor alpha (TNF-α), a pro-inflammatory cytokine secreted from the tissue on the insulin signaling pathway, can play a role in causing fat resistance to insulin in type 2 diabetes patients. Adiponectin is a specific protein of adipose tissue. It belongs to the collectin family, which is present in human plasma at a high level and can protect against vascular lesions. Considering the importance of epigenetic changes in the development of multifactorial diseases, this study was conducted to investigate the methylation of TNF-α gene promoter in patients with type diabetes with cardiovascular disease and compare it with diabetic people without cardiovascular disease. Also, the serum concentration of adiponectin was investigated in diabetic patients with and without cardiovascular disease. For this purpose, 95 patients with type 2 diabetes referred to Isfahan Endocrine and Metabolism Research Center were divided into two groups: cardiovascular disease and without cardiovascular disease, based on the angiography results. Serum adiponectin level was measured by the RIA method. In addition to adiponectin, indicators such as FBS, cholesterol, triglycerides, and HDL were also measured in these patients. Then, the promoter region of the TNF-α gene was investigated by bisulfite treatment method, nested PCR, and finally, sequence determination. The results showed that the serum level of adiponectin was higher in diabetic patients without cardiovascular disease than in diabetic patients with cardiovascular disease, but this difference was not statistically significant. Also, no change was observed between men and women in TNF-α gene promoter methylation in diabetic and non-diabetic groups. In general, the decrease in adiponectin concentration in diabetic people can be a factor in causing macroangiopathy, so it can be predicted that the production of recombinant adiponectin can be helpful in the treatment and protection of cardiovascular disease in these patients. Also, it seems that the epigenetic changes of cytokines that play a role in causing insulin resistance in type 2 diabetic patients are not noticeable in the peripheral blood sample. In this regard, other tissues should probably be investigated.

Atmospheric wet organic nitrogen deposition in China: Insights from the national observation network.

Organic nitrogen (N) is an important component of atmospheric reactive N deposition, and its bioavailability is almost as important as that of inorganic N. Currently, there are limited reports of national observations of organic N deposition; most stations are concentrated in rural and urban areas, with even fewer long-term observations of natural ecosystems in remote areas. Based on the China Wet Deposition Observation Network, this study regularly collected monthly wet deposition samples from 43 typical ecosystems from 2013 to 2021 and measured related N concentrations. The aim was to provide a more comprehensive assessment of the multi-component characteristics of atmospheric wet N deposition and reveal the influencing factors and potential sources of wet dissolved organic N (DON) deposition. The results showed that atmospheric wet deposition fluxes of NO3-, NH4+, DON and dissolved total N (DTN) were 4.68, 5.25, 4.32, and 13.05 kg N ha-1 yr-1, respectively, and that DON accounted for 30 % of DTN deposition (potentially up to 50 % in remote areas). Wet DON deposition was related to anthropogenic emissions (agriculture, biomass burning, and traffic), natural emissions (volatile organic compound emissions from vegetation), and precipitation processes. The wet DON deposition flux was higher in South, Central, and Southwest China, with more precipitation and intensive agricultural activities or more vegetation cover, and lower in Northwest China and Inner Mongolia, with less precipitation and human activities or vegetation cover. DON was the main contributor to DTN deposition in remote areas and was possibly related to natural emissions. In rural and urban areas, DON may have been more influenced by agricultural activities and anthropogenic emissions. This study quantified the long-term spatiotemporal patterns of wet N deposition and provides a reference for future N addition experiments and N cycle studies. Further consideration of DON deposition is required, especially in the context of anthropogenic control of NO2 and NH3.

Mind the Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation.

Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in this campaign relies upon aligning the distributions of source and target domain. One common attempt is to enforce the global alignment between two domains, which, however, ignores the fatal local-imbalance domain gap problem, i.e., some local features with larger domain gap are harder to transfer. Recently, some methods conduct alignment focusing on local regions to improve the efficiency of model learning. While this operation may cause a deficiency of critical information from contexts. To tackle this limitation, we propose a novel strategy to alleviate the domain gap imbalance considering the characteristics of medical images, namely Global-Local Union Alignment. Specifically, a feature-disentanglement style-transfer module first synthesizes the target-like source images to reduce the global domain gap. Then, a local feature mask is integrated to reduce the 'inter-gap' for local features by prioritizing those discriminative features with larger domain gap. This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency. We conduct a series of experiments with two cross-modality adaptation tasks, i,e. cardiac substructure and abdominal multi-organ segmentation. Experimental results indicate that our method achieves state-of-the-art performance in both tasks.

Ruthenium ion catalytic oxidation depolymerization of lignite under ultra-low dosage of RuCl3 catalyst and separation of the organic products with inorganic salts.

Depolymerization of lignite into valuable chemicals via ruthenium ion catalytic oxidation (RICO) is a potential route for the non-energy utilization of lignite. However, the high cost of the Ru catalyst during depolymerization and the high content of inorganic salts in the product solution limit the development of this route. In this work, RICO depolymerization of lignite was conducted under an ultra-low dosage of RuCl3 catalyst to decrease the usage of the catalyst during the RICO process. Different approaches were attempted to fulfill the separation of benzene polycarboxylic acids (BPCAs) products with the inorganic salts derived from the oxidant NaIO4, including butanone extraction and desalting via crystallization under different temperatures. The results show that lignite can be efficiently depolymerized under the mass ratio of RuCl3/lignite as low as 1/1000 by prolonging the reaction time without decreasing the depolymerization degree and BPCAs yields compared to the commonly used mass ratio of 1/10. Butanone can extract ca. 91% of the total BPCAs in the product solution, and the inorganic salts content (mainly NaIO3) in the extraction solution was as low as 0.19 mg mL-1. A new strategy of first acidification of depolymerization aqueous solution by HCl and then extraction by butanone is proved to be efficient for the separation of BPCAs with inorganic salts. Salting out via crystallization under lower temperature can remove ca. half content of the salts, and the efficiency is inferior to butanone extraction. The low usage of RuCl3 can efficiently decrease the catalyst cost of the RICO process, and butanone extraction can fulfill the enrichment of BPCAs and the separation of BPCAs with inorganic salts. This work is meaningful for the potential application of RCIO depolymerization of lignite for the production of valuable chemicals.

The Nurse-Physician Relationship During the COVID-19 Pandemic in Shanghai, China: Cross-sectional Study.

BACKGROUND: The nurse-physician relationship is important for the stability of collaboration. The COVID-19 pandemic has put unprecedented pressure on the health care system and has placed greater demands on nurse-physician collaboration. Nurses and physicians often struggle to share mutual responsibility and communicate effectively. OBJECTIVE: This study aimed to evaluate the relationship between nurses and physicians during the COVID-19 pandemic and construct a new model combining the attitude and behaviors of the 2 groups to assess various factors' impacts on job satisfaction and confrontational behavior. METHODS: We conducted this quantitative cross-sectional study to assess the relationship between nurses and physicians based on the attitudes and behaviors toward nurse-physician collaboration. We first investigated the satisfaction of nurses and physicians with their relationship and how they thought the COVID-19 pandemic had affected that relationship. We used an adapted and modified Jefferson Scale of Attitudes Toward Physician-Nurse Collaboration questionnaire that consisted of 17 items under 5 dimensions. Structural equation modeling was used to assess the relationships between domains. Ordinal logistic regression was used to evaluate the relationship between different domains of the questionnaire and the satisfaction of the current nurse-physician relationship. RESULTS: We included a total of 176 nurses and 124 physicians in this study. Compared to 7.2% (9/124) of physicians, 22.7% (40/176) of nurses were dissatisfied with the current nurse-physician relationship. Most physicians (101/124, 81.5%) and nurses (131/176, 74.5%) agreed that the nurse-physician relationship had become better because of the COVID-19 pandemic and that the public had greater respect for them. However, significantly fewer nurses (59/176, 33.5% vs 79/124, 63.7%; P<.001) thought that physicians and nurses were treated with the same respect. Nurses scored significantly higher scores in caring versus curing (mean 16.27, SD 2.88 vs mean 17.43, SD 2.50; P<.001) and physician's authority (mean 8.72, SD 3.21 vs mean 7.24, SD 3.32; P<.001) subscales compared with physicians. The shared education and collaboration subscale had a significantly positive relationship with the nurse's autonomy subscale (standardized coefficient=0.98; P<.001). Logistic regression showed that 4 subscales (shared education and collaboration: P<.001; caring versus curing: P<.001; nurse's autonomy: P<.001; and confrontation: P=.01) were significantly associated with the level of satisfaction of the current nurse-physician relationship. CONCLUSIONS: This study showed that nurses were more dissatisfied with the current nurse-physician relationship than physicians in Shanghai. Policy makers and managers in the medical and educational system should emphasize an interprofessional collaboration between nurses and physicians. Positive attitudes toward shared collaboration and responsibility may help to improve the relationship between the 2 parties.

High environmental costs behind rapid economic development: Evidence from economic loss caused by atmospheric acid deposition.

The rapid growth of energy-intensive and high-emission industries has propelled China's economy but has also led to massive levels of air pollutant emissions and ecological problems, such as acid deposition. Despite recent declines, atmospheric acid deposition in China is still severe. Long-term exposure to high levels of acid depositions has a substantial negative impact on the ecosystem. Evaluating these hazards and incorporating this issue into planning and decision-making processes is critical to achieving sustainable development goals in China. However, the long-term economic loss caused by atmospheric acid deposition and its temporal and spatial variation in China is unclear. Hence, the aim of this study was to assess the environmental cost of acid deposition in the agriculture, forestry, construction, and transportation industries from 1980 to 2019, using long-term monitoring, integrated data, and the dose-response method with localization parameters. The results showed that the estimated cumulative environmental cost of acid deposition was USD 230 billion, representing 0.27% of the gross domestic product (GDP) in China. This cost, was particularly high for building materials, followed by crops, forests, and roads. Temporally, the environmental cost and the ratio of environmental cost to GDP decreased from their peaks by 43% and 91%, respectively, because of emission controls targeting acidifying pollutants and promotion of clean energy. Spatially, the largest environmental cost occurred in developing provinces, indicating that more stringent emission reduction measures should be implemented in these regions. These findings highlight the huge environmental costs behind rapid development; however, the implementation of reasonable emission reduction measures can effectively reduce these environmental costs, providing a promising paradigm for other undeveloped and developing countries.

Globally Adaptive Neural Network Output-Feedback Control for Uncertain Nonlinear Systems.

In this article, a globally neural-network-based adaptive control strategy with flat-zone modification is proposed for a class of uncertain output feedback systems with time-varying bounded disturbances. A high-order continuously differentiable switching function is introduced into the filter dynamics to achieve global compensation for uncertain functions, thus further to ensure that all the closed-loop signals are globally uniformity ultimately bounded (GUUB). It is proven that the output tracking error converges to the prespecified neighborhood of the origin. The effectiveness of the proposed control method is verified by two simulation examples.

Globally Adaptive Neural Network Tracking for Uncertain Output-Feedback Systems.

This article investigates the problem of global neural network (NN) tracking control for uncertain nonlinear systems in output feedback form under disturbances with unknown bounds. Compared with the existing NN control method, the differences of the proposed scheme are as follows. The designed actual controller consists of an NN controller working in the approximate domain and a robust controller working outside the approximate domain, in addition, a new smooth switching function is designed to achieve the smooth switching between the two controllers, in order to ensure the globally uniformly ultimately bounded of all closed-loop signals. The Lyapunov analysis method is used to strictly prove the global stability under the combined action of unmeasured states and system uncertainties, and the output tracking error is guaranteed to converge to an arbitrarily small neighborhood through a reasonable selection of design parameters. A numerical example and a practical example were put forward to verify the effectiveness of the control strategy.

Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning.

Annual gross primary productivity (AGPP) is the basis for grain production and terrestrial carbon sequestration. Mapping regional AGPP from site measurements provides methodological support for analysing AGPP spatiotemporal variations thereby ensures regional food security and mitigates climate change. Based on 641 site-year eddy covariance measuring AGPP from China, we built an AGPP mapping scheme based on its formation and selected the optimal mapping way, which was conducted through analysing the predicting performances of divergent mapping tools, variable combinations, and mapping approaches in predicting observed AGPP variations. The reasonability of the selected optimal scheme was confirmed by assessing the consistency between its generating AGPP and previous products in spatiotemporal variations and total amount. Random forest regression tree explained 85 % of observed AGPP variations, outperforming other machine learning algorithms and classical statistical methods. Variable combinations containing climate, soil, and biological factors showed superior performance to other variable combinations. Mapping AGPP through predicting AGPP per leaf area (PAGPP) explained 86 % of AGPP variations, which was superior to other approaches. The optimal scheme was thus using a random forest regression tree, combining climate, soil, and biological variables, and predicting PAGPP. The optimal scheme generating AGPP of Chinese terrestrial ecosystems decreased from southeast to northwest, which was highly consistent with previous products. The interannual trend and interannual variation of our generating AGPP showed a decreasing trend from east to west and from southeast to northwest, respectively, which was consistent with data-oriented products. The mean total amount of generated AGPP was 7.03 ± 0.45 PgC yr-1 falling into the range of previous works. Considering the consistency between the generated AGPP and previous products, our optimal mapping way was suitable for mapping AGPP from site measurements. Our results provided a methodological support for mapping regional AGPP and other fluxes.

Risk factors for pulmonary complications after laparoscopic liver resection: a multicenter retrospective analysis.

BACKGROUND: Postoperative pulmonary complications (PPCs) are among the most common complications after liver resection. Although the application of laparoscopy has reduced the incidence of PPCs, the rate of PPCs after laparoscopic liver resection (LLR) remains high and the risk factors for the same are unclear. Therefore, this study aimed to determine the risk factors for PPCs after LLR. METHODS: In this multicenter study, 296 patients underwent LLR from January 2019 to December 2020. Demographic data, pathological variables, and perioperative variables were reviewed. Univariate and multivariate analyses were performed to identify the independent risk factors for PPCs. RESULTS: Of the 296 patients, 80 (27.0%) developed PPCs. Patients with PPCs had significantly increased total costs, operation costs, length of stays, and postoperative hospital stays. Multivariate analysis identified three independent risk factors for PPCs after LLR: smoking [Odds ratio (OR): 5.413, 95% confidence intervals (CI): 2.446-11.978, P = < 0.001], location of lesion in segment 7 or 8 (OR 3.134, 95% CI 1.593-6.166, P = 0.001), duration of liver ischemia (OR 1.038, 95% CI 1.022-1.054, P < 0.001), and presence of intraoperative hypothermia (OR 3.134, 95% CI 1.593-6.166, P < 0.001). CONCLUSION: Smoking, location of lesion in segment 7 or 8, duration of liver ischemia and intraoperative hypothermia were independent risk factors for PPCs which significantly increased the length of stays and burden of healthcare costs.

Multi-Scope Feature Extraction for Intracranial Aneurysm 3D Point Cloud Completion.

3D point clouds are gradually becoming more widely used in the medical field, however, they are rarely used for 3D representation of intracranial vessels and aneurysms due to the time-consuming data reconstruction. In this paper, we simulate the incomplete intracranial vessels (including aneurysms) in the actual collection from different angles, then propose Multi-Scope Feature Extraction Network (MSENet) for Intracranial Aneurysm 3D Point Cloud Completion. MSENet adopts a multi-scope feature extraction encoder to extract the global features from the incomplete point cloud. This encoder utilizes different scopes to fuse the neighborhood information for each point fully. Then a folding-based decoder is applied to obtain the complete 3D shape. To enable the decoder to intuitively match the original geometric structure, we engage the original points coordinates input to perform residual linking. Finally, we merge and sample the complete but coarse point cloud from the decoder to obtain the final refined complete 3D point cloud shape. We conduct extensive experiments on both 3D intracranial aneurysm datasets and general 3D vision PCN datasets. The results demonstrate the effectiveness of the proposed method on three evaluation metrics compared to baseline: our model increases the F-score to 0.379 (+21.1%)/0.320 (+7.7%), reduces Chamfer Distance score to 0.998 (-33.8%)/0.974 (-6.4%), and reduces the Earth Mover's Distance to 2.750 (17.8%)/2.858 (-0.8%).

Eddy covariance measurement-based differences in annual evapotranspiration between forests and grasslands in China.

Annual evapotranspiration (AET), the total water vapor loss to the atmosphere during a year, is a vital process of global water cycles and energy cycles. Revealing the differences in AET values and spatial variations between forests and grasslands would benefit for understanding AET spatial variations, which serves as a basis for regional water management. Based on published eddy covariance measurements in China, we collected AET values from 29 forests and 46 grasslands, and analyzed the differences in AET values and spatial variations between forests and grasslands in China. The results showed that forests had a significant higher AET (645.98 ± 232.73 kgH2O m-2 yr-1) than grasslands (359.31 ± 156.02 kgH2O m-2 yr-1), while the difference in AET values between forests and grasslands was not significant after controlling mean annual precipitation (MAP) relating factors. The effects of latitude and mean annual air temperature (MAT) on AET spatial variations differed between forests and grassland, while AET of forests and grasslands both exhibited increasing trends with similar rates along the increasing MAP, aridity index (AI), soil water content (SW), and leaf area index. The comprehensive effects of multiple factors on AET spatial variations differed between forests and grasslands, while MAP both played a dominating role. The effects of other factors were achieved through their close correlations with MAP. Therefore, forests and grasslands under similar climate had comparable AET values. AET responses to MAP were comparable between ecosystem types. Our findings provided a data basis for understanding AET spatial variation over terrestrial ecosystems of China or globally.

Eddy covariance-based differences in net ecosystem productivity values and spatial patterns between naturally regenerating forests and planted forests in China.

Net ecosystem productivity (NEP), the difference between gross primary productivity (GPP) and ecosystem respiration (ER), is the basis of forest carbon sinks. Revealing NEP differences between naturally regenerating forests (NF) and planted forests (PF) can benefit for making carbon neutrality strategies. Based on 35 eddy covariance measurements in China, we analyzed NEP differences in values and spatial patterns between NF and PF. The results showed that NF had slightly lower NEP than PF, resulting from the high stand age (SA) and soil fertilizer, while their differences were not significant (p > 0.05). The increasing latitude decreased mean annual air temperature thus decreased GPP both in NF and PF. However, the higher SA and soil fertilizer in NF made most GPP release as ER thus induced no significant NEP spatial variation, while lower SA and soil fertilizer in PF made NEP spatially couple with GPP thus showed a decreasing latitudinal pattern. Therefore, stand characteristics determined the differences in NEP values but indirectly affected the differences in NEP spatial variations through altering GPP allocation. The decreasing latitudinal pattern of NEP in PF indicates a higher sequestration capacity in the PF of South China. Our results provide a basis for improving the forest carbon sequestration.

Atmospheric Wet Iron, Molybdenum, and Vanadium Deposition in Chinese Terrestrial Ecosystems.

Iron (Fe), molybdenum (Mo), and vanadium (V) are the main components of the three known biological nitrogenases, which constrain nitrogen fixation and affect ecosystem productivity. Atmospheric deposition is an important pathway of these trace metals into ecosystems. Here, we explored the deposition flux, spatiotemporal pattern, and influencing factors of atmospheric wet Fe, Mo, and V deposition based on China Wet Deposition Observation Network (ChinaWD) data from 2016 to 2020. Our results showed that atmospheric wet Fe, Mo, and V deposition was 7.77 ± 7.24, 0.16 ± 0.11, and 0.13 ± 0.12 mg m-2 a-1 in Chinese terrestrial ecosystems, respectively, and revealed obvious spatial patterns but no significant annual trends. Wet Fe deposition was significantly correlated with the soil Fe content. Mo and V deposition was more affected by anthropogenic activities than Fe deposition. Wet Mo deposition was significantly affected by Mo ore reserves and waste incineration. V deposition was significantly correlated with domestic biomass burning. This study quantified wet Fe, Mo, and V deposition in China for the first time, and the implications of atmospheric trace metal deposition on biological nitrogen fixation were discussed.

Atmospheric silicon wet deposition and its influencing factors in China.

Silicon (Si) is considered a "quasi-essential" nutrient element for plants and is also an essential nutrient for some phytoplankton. Except for the silicate provided by weathering, atmospheric deposition has gradually become an important supplementary method for Si nutrients to enter the ecosystem. However, national observational studies on atmospheric silicon deposition have not yet been reported. Herein, based on the China Wet Deposition Observation Network, we continuously collected monthly wet deposition samples from 43 typical ecosystems from 2013 to 2020 and measured the content of dissolved silica (dSi) in precipitation to quantify the spatiotemporal patterns of Si wet deposition in China. The results showed that the mean annual dSi wet deposition in China during 2013-2020 was approximately 2.07 ± 0.27 kg ha-1 yr-1. Atmospheric dSi deposition was higher in Southwest, North, and South China but lower in the Northwest and Northeast China, which was mainly regulated by precipitation and soil available Si content. There was no significant annual variation trend in dSi deposition during 2013-2020 in China, which showed disorderly fluctuations from year to year. This study revealed the spatiotemporal patterns of atmospheric dSi deposition in China for the first time, which can provide unique scientific data to explore the potential effect of dSi deposition on carbon sequestration in aquatic ecosystems. A comprehensive evaluation of the nutrient balance of aquatic ecosystems from the perspective of nitrogen, phosphorus, and silicon stoichiometry is required in the future.

Effects of ecosystem types on the spatial variations in annual gross primary productivity over terrestrial ecosystems of China.

Annual gross primary productivity (AGPP) serves as the basis for forming biomass and carbon sinks. Analysing the effects of ecosystem types on AGPP spatial variations would be beneficial for clarifying the spatial variability in AGPP, which would serve ecological management practices such as ensuring regional food security. Based on published eddy covariance measurements in China, we collected AGPP data from 128 ecosystems and analysed the effects of ecosystem types on the spatial variations in AGPP to reveal the AGPP spatial variability and its influencing factors over terrestrial ecosystems of China. The results showed that AGPP significantly differed among ecosystem types and vegetation regions, with the lowest AGPP appearing in grasslands, while different ecosystem types had comparable AGPP within the same vegetation region. The AGPP of all ecosystem types showed a decreasing latitudinal trend but slight longitudinal and altitudinal trends. Mean annual air temperature (MAT) and mean annual precipitation (MAP) were found to affect the spatial variations in AGPP over most ecosystem types, while other factors played little role. The mean annual leaf area index (LAI) and the maximum leaf area index (MLAI) were also found to affect the spatial variations in AGPP over most ecosystem types. Factors influencing the AGPP spatial variations differed among ecosystem types, but all included climatic and biotic factors. Therefore, climate inducing spatial distribution of ecosystem types and the non-zonal water supply made AGPP values and factors affecting the spatial variations in AGPP differ among ecosystem types, while different ecosystem types within the same vegetation region had comparable AGPP values. The spatial variation in AGPP over terrestrial ecosystems of China resulted from the integrated effects of climatic and biotic factors. Our study provided data support for improving the understanding of global AGPP spatial variability.