High-Resolution Estimates of N2O Emissions from Inland Waters and Wetlands in China.

Inland waters (rivers, lakes, and reservoirs) and wetlands (marshes and coastal wetlands) represent large and continuous sources of nitrous oxide (N2O) emissions, in view of adequate biomass and anaerobic conditions. Considerable uncertainties remain in quantifying spatially explicit N2O emissions from aquatic systems, attributable to the limitations of models and a lack of comprehensive data sets. Herein, we conducted a synthesis of 1659 observations of N2O emission rates to determine the major environmental drivers across five aquatic systems. A framework for spatially explicit estimates of N2O emissions in China was established, employing a data-driven approach that upscaled from site-specific N2O fluxes to robust multiple-regression models. Results revealed the effectiveness of models incorporating soil organic carbon and water content for marshes and coastal wetlands, as well as water nitrate concentration and dissolved organic carbon for lakes, rivers, and reservoirs for predicting emissions. Total national N2O emissions from inland waters and wetlands were 1.02 × 105 t N2O yr-1, with contributions from marshes (36.33%), rivers (27.77%), lakes (25.27%), reservoirs (6.47%), and coastal wetlands (4.16%). Spatially, larger emissions occurred in the Songliao River Basin and Continental River Basin, primarily due to their substantial terrestrial biomass. This study offers a vital national inventory of N2O emissions from inland waters and wetlands in China, providing paradigms for the inventorying work in other countries and insights to formulate effective mitigation strategies for climate change.

Identifying Key Sources for Air Pollution and CO2 Emission Co-control in China.

China is confronting the dual challenges of air pollution and climate change, mandating the co-control of air pollutants and CO2 emissions from their shared sources. Here we identify key sources for co-control that prioritize the mitigation of PM2.5-related health burdens, given the homogeneous impacts of CO2 emissions from various sources. By applying an integrated analysis framework that consists of a detailed emission inventory, a chemical transport model, a multisource fused dataset, and epidemiological concentration-response functions, we systematically evaluate the contribution of emissions from 390 sources (30 provinces and 13 socioeconomic sectors) to PM2.5-related health impacts and CO2 emissions, as well as the marginal health benefits of CO2 abatement across China. The estimated source-specific contributions exhibit substantial disparities, with the marginal benefits varying by 3 orders of magnitude. The rural residential, transportation, metal, and power and heating sectors emerge as pivotal sources for co-control, with regard to their relatively large marginal benefits or the sectoral total benefits. In addition, populous and heavily industrialized provinces such as Shandong and Henan are identified as the key regions for co-control. Our study highlights the significance of incorporating health benefits into formulating air pollution and carbon co-control strategies for improving the overall social welfare.

An integrated model for simulating and diagnosing the water quality based on the system dynamics and Bayesian network.

An integrated model for simulating and diagnosing water quality based on the system dynamics and Bayesian network (BN) is presented in the paper. The research aims to connect water monitoring downstream with outlet management upstream in order to present an efficiency outlet management strategy. The integrated model was built from two components: the system dynamics were used to simulate the water quality and the BN was applied to diagnose the reason for water quality deterioration according to the water quality simulation. The integrated model was applied in a case study of the Songhua River from the Baiqi section to the Songlin section to prove its reasonability and accuracy. The results showed that the simulation fit to the variation trend of monitoring data, and the average relative error was less than 10%. The water quality deterioration in the Songlin section was mainly found to be caused by the water quality in the upper reach and Hadashan Reservoir drain by using the diagnosis function of the integrated model based on BN. The relevant result revealed that the integrated model could provide reasonable and quantitative support for the basin manager to make a reasonable outlet control strategy to avoid more serious water quality deterioration.

Temporal trend and driving effect of demographic transitions on embedded carbon emissions of Chinese households.

A population is regarded as the main non-economic driver of carbon emissions, causing the climatic crisis, especially in China experiencing a dramatic demographic transition. In contrast to aging, low fertility, the most remarkable feature of the Chinese population transition, has always been ignored when evaluating carbon emissions, due to the lack of long-run data. To narrow this gap, an integrated framework combining the continuous input-output tables from 1997 to 2018 with the Mann-Kendall test and vector auto-regression was presented to clarify the fluctuating trend of household embedded carbon emissions and the driving pattern of low fertility, aging, and urbanization. Our main findings showed that changes in household embedded carbon emissions have increased sharply in the last two decades. The growth of Chinese household embedded carbon emissions began to accelerate in 2001, which lagged 1 year behind the demographic indicators. Low fertility has a positive impact on households' embedded carbon emissions. More importantly, the impact of low fertility is more significant and far-reaching than that of aging. These suggest that aggressive policies for stimulating fertility and low-carbon lifestyles should be considered by policy makers.

Tracking Key Industrial Sectors for CO2 Mitigation through the Driving Effects: An Attribution Analysis.

The heavy pressure to improve CO2 emission control in industry requires the identification of key sub-sectors and the clarification of how they mitigate CO2 emissions through various actions. Focusing on 30 Chinese provincial regions, this study quantifies the contribution of each industrial sector to regional CO2 mitigation by combining the logarithmic mean Divisia index with attribution analysis and extract the key sectors of CO2 mitigation for each region. Results indicate that during 2010-2019, significant emission reduction was achieved through energy intensity (74%) in Beijing, while emission reductions were attained through industrial structure changes for Anhui (50%), Henan (45%), and Chongqing (45%). The contribution to emission reduction through energy structures is not significant. The production and supply of power and heat (PSPH) is a central factor in CO2 mitigation through all three inhibitive factors. Petroleum processing and coking (PPC) generally contributes to emission reduction through energy structures, while the smelting and pressing of ferrous metals (SPMF) through changes in industrial structures and energy intensity. PSPH and SPMF, in most regions, have not achieved the emission peak. Except in the case of coal mining and dressing (CMD), CO2 emissions in other key sectors have almost been decoupled from industrial development. CMD effectively promotes CO2 mitigation in Anhui, Henan, and Hunan, with larger contribution of PPC in Tianjin, Xinjiang, Heilongjiang, and that of smelting and pressing of nonferrous metals in Yunnan and Guangxi. The findings help to better identify key sectors across regions that can mitigate CO2 emissions, while analyzing the critical emission characteristics of these sectors, which can provide references to formulating region- and sector-specific CO2 mitigation measures for regions at different levels of development.

The effect of polymorphisms (M235T and T174M) on the angiotensinogen gene (AGT) in coronary artery disease in the Eastern Asian population: A systematic review and meta-analysis.

BACKGROUND: It is thought that genetic factors may play an important role in the development of coronary artery disease (CAD). Several studies report that AGT polymorphism is implicated in CAD susceptibility, but these results contradict those of the other studies with the associations being unclear in the Eastern Asian population. Therefore, meta-analysis was performed to evaluate this relationship. METHODS: Publication databases were used to search for eligible relevant studies and valid data were extracted from studies meeting the inclusion criteria. Subsequently, odds ratios (ORs) with 95 % confidence intervals (CIs), were used to assess the strength of the association between AGT polymorphism and CAD risk. RESULTS: Seven eligible studies published only in English were included in the present meta-analysis. In the Eastern Asian population, CAD susceptibility was shown to be related to AGT M235T under the heterozygote model (OR = 0.19). Stratified analysis indicated there was a significant relationship between AGT M235T and CAD risk in China under allelic (OR = 1.34), dominant (OR = 1.43), and heterozygote (OR = 1.62) models. The results showed that the T174M polymorphism was significantly associated with CAD risk in recessive (OR = 2.28) and homozygote (OR = 2.37) models in the Eastern Asian population. CONCLUSIONS: In the Eastern Asian population, especially the Chinese, the M235T of AGT is associated with CAD susceptibility. The T174M polymorphisms were associated with CAD risk in the Eastern Asian population.

Dynamic Driving Mechanism of Dual Structural Effects on the Correlation between Economic Growth and CO2 Emissions: Evidence from a Typical Transformation Region.

How will the dual structural effects, represented by industrial structure and energy structure, affect the future correlation between economic growth and CO2 emissions? Taking Jilin Province as an example, this study explores the dynamic driving mechanism of dual structural effects on the correlation between economic growth and CO2 emissions by innovatively building an integrated simulation model from 1995 to 2015 and setting different scenarios from 2016 to 2050. Correspondingly, the concept of marginal utility and the method of variance decomposition analysis are introduced to reveal the mechanism. The results show that the energy structure is different while the industrial structure tends to be similar when CO2 emissions reach the peak under different scenarios. The slower the dual structure adjustment, the more significant the upward trend appears before the peak. The contribution of the dual structural effects to CO2 emissions caused by unit GDP growth is basically the same in peak year. With the transformation of socio-economy, the positive driving effect of the industrial structure will gradually weaken, while the negative driving effect of the energy structure will gradually increase. The methods and results presented can provide insights into sensible trade-offs of CO2 emissions and economic growth in different countries/regions during structural transitions.

Carbon Mitigation Pathways of Urban Transportation under Cold Climatic Conditions.

Climate heterogeneity has enormous impacts on CO2 emissions of the transportation sector, especially in cold regions where the demand for in-car heating and anti-skid measures leads to high energy consumption, and the penetration rate of electric vehicles is low. It entails to propose targeted emission reduction measures in cold regions for peaking CO2 emissions as soon as possible. This paper constructs an integrated long-range energy alternatives planning system (LEAP) model that incorporates multi-transportation modes and multi-energy types to predict the CO2 emission trend of the urban transportation sector in a typical cold province of China. Five scenarios are set based on distinct level emission control for simulating the future trends during 2017-2050. The results indicate that the peak value is 704.7-742.1 thousand metric tons (TMT), and the peak time is 2023-2035. Energy-saving-low-carbon scenario (ELS) is the optimal scenario with the peak value of 716.6 TMT in 2028. Energy intensity plays a dominant role in increasing CO2 emissions of the urban transportation sector. Under ELS, CO2 emissions can be reduced by 68.66%, 6.56% and 1.38% through decreasing energy intensity, increasing the proportion of public transportation and reducing the proportion of fossil fuels, respectively. Simultaneously, this study provides practical reference for other cold regions to formulate CO2 reduction roadmaps.

Uncertain Water Environment Carrying Capacity Simulation Based on the Monte Carlo Method-System Dynamics Model: A Case Study of Fushun City.

Water environment carrying capacity (WECC) is an effective indicator that can help resolve the contradiction between social and economic development and water environment pollution. Considering the complexity of the water environment and socioeconomic systems in Northeast China, this study establishes an evaluation index system and a system dynamics (SD) model of WECC in Fushun City, Liaoning, China, through the combination of the fuzzy analytic hierarchy process and SD. In consideration of the uncertainty of the future development of society, the Monte Carlo and scenario analysis methods are used to simulate the WECC of Fushun City. Results show that if the current social development mode is maintained, then the WECC in Fushun will have a slow improvement in the future, and a "general" carrying state with a WECC index of 0.566 in 2025 will be developed. Moreover, focusing on economic development (Scheme 1 with a WECC index of [0.22, 0.45] in 2025) or environmental protection (Scheme 2 with a WECC index of [0.48, 0.68] in 2025) cannot effectively improve the local water environment. Only by combining the two coordinated development modes (Scheme 3) can WECC be significantly improved and achieve "general" or "good" carrying state with a WECC index of [0.59, 0.79]. An important development of this study is that the probability of each scheme's realization can be calculated after different schemes are formulated. In turn, the feasibility of the scheme will be evaluated after knowing the probability, so as to determine the path suitable for local development. This is of great significance for future urban planning.

Association of CYP4F2 and CTRP9 polymorphisms and serum selenium levels with coronary artery disease.

Aims to explore the interaction between serum selenium level and CYP4F2 and CTRP9 gene polymorphisms in the development of coronary artery disease (CAD).A total of 200 cases of CAD were selected from the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Hubei, China, and 200 healthy subjects cases were served as controls. The polymorphism of CYP4F2 and CTRP9 gene was detected by Sanger sequencing, and the serum selenium level was measured by hydride generation atomic fluorescence spectrometry.The serum selenium level in the CAD group was significantly lower than that in the control group. The risk of CAD was decreased in the patients carrying the AA genotype in CYP4F2 rs3093135, while the frequency of the CC genotype of CTRP9 rs9553238 in CAD patients was higher than that in control subjects. Low serum selenium level and CTRP9 rs9553238 CC genotype play a positive role in the occurrence of CAD.The serum selenium level is negatively correlated with CAD. The polymorphism of the CYP4F2 rs3093135 and CTRP9 rs9553238 was significantly related to the susceptibility of CAD, and there is a synergistic effect between the serum selenium level and the CTRP9 rs9553238 CC genotype, which significantly increases the risk of CAD.

An Optimization Model for a Wetland Restoration Project under Uncertainty.

Restoring natural wetlands with conservation projects is an urgent task for human well-being. This paper introduces the Interval linear programming (ILP) method in wetland restoration projects for the first time and builds an optimization model. The purpose of the optimization model is to find an optimal restoration measures allocation pattern that can minimize the total investment in wetland restoration projects and obtain additional ecological environment and socio-economic benefits. The optimization model can also decrease the influence of interval uncertainty in the system by expressing the executed solution as interval numbers with an upper bound and a lower bound. The result of the optimization model for the wetland restoration project indicated a range of 6.84%⁻15.43% reduction on comparison with the original scheme which verified the effectiveness and validity of this optimization model. Our findings indicate that higher ecological and social benefits of wetland restoration projects can be achieved with lower restoration investment on the application of the reasonable and optimal restoration measures allocation pattern by the optimization model. The results of interval solutions can provide guidance for project managers to select a satisfactory decision-making plan by adjusting the decision variables in the interval solutions according to the practical situation. It can be seen that reeds were suggested to be planted over 46.75 km², with the same lower bound and higher bound. Meanwhile, populus euphratica, and dryland willow were recommended to be planted in a mixed forest pattern within the interval of 30.54 km² to 37.25 km², and so forth. With the optimal solutions obtained from the model, the total project investment would be in the range of 2193.14 (104 CNY) to 2416.01 (104 CNY). Future improvements of our optimization model in wetland restoration projects should consider other kinds of uncertainties in the system such as stochastic uncertainties, fuzzy uncertainties, and integrated uncertainties.

Hepatic stellate cell contraction is inhibited by lipo-prostaglandin E1 in vitro.

Prostaglandin E1 (PGE1 ) has been reported to have, experimentally and clinically, a protective effect against liver damage. This effect may result from the relaxation of hepatic stellate cells, whose contraction induces vasoconstriction of hepatic sinusoids. However, prostaglandins are unstable and a new drug delivery system is necessary to administer a sufficient amount of prostaglandin to achieve a protective effect in the liver. The aim of the study is to investigate the effects of lipo-prostaglandin E1 (lipo-PGE1 ) which has a novel drug delivery system on the stellate cell contraction induced by endothelin-1 in vitro. Lipo-PGE1 inhibited endothelin-1-induced stellate cell contraction in concentrations of 10, 30 and 50 ng/mL. Therefore, lipo-PGE1 may show a cytoprotective effect in the liver through the relaxation of stellate cells and an increase in the hepatic sinusoidal blood flow.

The novel histamine H2 receptor antagonist FRG-8813 prevents delay of wound repair induced by hydrogen peroxide in a rabbit gastric epithelial cell system.

The effects of a novel histamine H2 receptor antagonist (FRG-8813) on the restoration process of gastric epithelial wounds were assessed using an in vitro wound healing model. FRG-8813 (1, 10 mol/L) was added to a complete confluent monolayer cell sheet after artificial wounding. The restoration process was analysed by a time-lapse video system and cell migration, proliferation and apoptosis were assessed. Hydrogen peroxide (1, 3 mmol/L) inhibited restoration after wounding by suppressing cell migration and proliferation and induced epithelial cell apoptosis around the wound. The addition of FRG-8813 abolished the hydrogen peroxide-induced retardation and prevented apoptosis, although FRG-8813 itself did not enhance wound healing. FRG-8813 may act as a radical scavenger as well as having an anti-secretory action and may have favourable effects on peptic ulcer healing.