Straw return and tillage depth treatments are one of the most important agricultural management measures that affect farmland soil respiration, but the mechanism of their interaction affecting farmland soil respiration remains unclear. Therefore, 116 published research articles were used through Meta-analysis technology for dryland farmland ecosystems in China to explore the effects of straw return and tillage depth treatments and their interaction on farmland soil respiration and its regulatory factors, which will provide important data support and a theoretical basis for achieving "carbon neutrality" in farmland ecosystems. The results showed that no tillage reduced soil respiration by 8.3%, and the effects of shallow and deep tillage treatments on soil respiration were not significant, but the increase in soil respiration still showed a trend of deep tillage>shallow tillage>no tillage. However, both shallow and deep tillage had relatively small effects on soil respiration and soil organic carbon (SOC), whereas no tillage reduced soil respiration by 8.3% and increased SOC by 7.05%. Therefore, implementing no tillage measures is of great significance for soil carbon sequestration and emission reduction in farmland ecosystems. In addition, tillage depth significantly regulated the impact of straw return on soil respiration, and the increase in soil respiration showed a trend of deep tillage straw return>shallow tillage straw return>no tillage straw return, with an overall average increase of 14.51%. The increase in soil respiration under different tillage depth treatments after straw return was closely related to the change in soil bulk density, crop yield, SOC, soil temperature, and moisture, and the contribution to the increase in soil respiration showed a trend of soil bulk density>crop yield>soil organic carbon>soil moisture>soil temperature. However, SOC increased by 29.32%, 10.12%, and 23.94%, respectively, in the deep tillage straw return, shallow tillage straw return, and no tillage straw return treatments, whereas soil respiration increased by 29.32% and 18.92%, respectively, in the deep tillage straw return and shallow tillage straw return treatments, and it only increased by 1.2% in the no tillage straw return treatment. Therefore, no tillage straw return was also beneficial to soil carbon sequestration and emission reduction in farmland ecosystems. Thus, in the dryland farmland ecosystem of China, tillage depth treatments regulated the impact of straw return on soil respiration, which was mainly related to soil physical and chemical properties, especially being closely related to soil bulk density. Moreover, no tillage and no tillage straw return are important agricultural management measures that are conducive to soil carbon sequestration and emission reduction.

Subsurface dams have been recognized as one of the most effective measures for preventing saltwater intrusion. However, it may result in large amounts of residual saltwater being trapped upstream of the dam and take years to decades to remove, which may limit the utilization of fresh groundwater in coastal areas. In this study, field-scale numerical simulations were used to investigate the mechanisms of residual saltwater removal from a typical stratified aquifer, where an intermediate low-permeability layer (LPL) exists between two high-permeability layers, under the effect of seasonal sea level fluctuations. The study quantifies and compares the time of residual saltwater removal (Tre) for constant sea level (CSL) and seasonally varying sea level (FSL) scenarios. The modelling results indicate that, in most cases, seasonal fluctuations in sea level facilitate the dilution of residual saltwater and thus accelerate residual saltwater removal compared to a static sea level scenario. However, accounting for seasonal sea level variations may increase the required critical dam height (the minimum dam height required to achieve complete residual saltwater removal). Sensitivity analyses show that Tre decreases with increasing height of subsurface dam (Hd) under CSL or weaker sea level fluctuation scenarios; however, when the magnitude of sea level fluctuation is large, Tre changes non-monotonically with Hd. Tre decreases with increasing distance between subsurface dam and ocean for both CSL and FSL scenarios. We also found that stratification model had a significant effect on Tre. The increase in LPL thickness for both CSL and FSL scenarios leads to a decrease in Tre and critical dam height. Tre generally shows a non-monotonically decreasing trend as LPL elevation increases. These quantitative analyses provide valuable insights into the design of subsurface dams in complex situations.

To reduce the adverse influence of sudden water pollution accidents, it is essential to estimate the unknown contaminant source information (normally including the source location, initial release time, and total release mass) as soon as possible. The ensemble Kalman filter (EnKF) has been proven to be an effective algorithm for such an inverse problem. This paper proposes a new method based on EnKF to identify contaminant source information. The method we called RC-EnKF uses the relation coefficient of concentration instead of timely concentration as a state variable in the assimilation process. The advantage lies in the release source mass that can be decoupled from the parameter group of unknown contaminant information to improve the assimilation speed and reduce interference with the accuracy of assimilation. Two categories of cases are employed for validating the applicability and testing the performance compared with the traditional EnKF method in detail. Sensitivity analyses are carried out with different observation errors, number of observation sites, number of ensemble realizations, and model grid size. The results demonstrate that with the uncertain error of observation data, the RC-EnKF works nicely and shows superiority to the traditional EnKF method reflected in the strong immunity to interference from observation data errors and elevated efficiency with the requirement of fewer observation sites, ensemble realizations, and model grids. It illustrates that the RC-EnKF is a more efficient and robust method for estimating unknown contaminant source information.

This paper investigates the effects of the cutoff wall on the fate of nitrate (NO 3 - ), the NO 3 - removal rate, and the salinity distribution in a coastal aquifer under tidal action. A numerical study was performed based on a coupled model with variable-saturation and variable-density flow and a convection-diffusion-reaction equation for solute transport in a coastal unconfined aquifer. The results showed that the cutoff wall led to a larger upper salinity plume (USP) and that the saltwater wedge (SW) further retreated seaward. The recirculation pathways of saltwater and groundwater were largely modified by the wall. The cutoff wall within the tidal range could increase the NO 3 - mass of denitrification and the NO 3 - removal efficiency and decrease the length of the SW and the freshwater flux. This modification of the saltwater and groundwater recirculation pathways was enhanced with increasing wall depth. A deeper cutoff wall led to a further retreated SW, lower freshwater flux, and greater improvements in the NO 3 - mass of denitrification and the NO 3 - removal efficiency. In addition, the cutoff wall significantly decreased the terrestrial dissolved organic carbon (T DOC ) discharge into the sea. Dissolved organic carbon source (S DOC ) promoted a higher NO 3 - removal efficiency. This study provides us with a better understanding of coastal physical-biogeochemical processes and dynamic mechanisms, as well as a guide for designing engineering measures to mitigate NO 3 - contamination and thus enhance groundwater quality management.

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees' extraction methods, the NDVI-CV method showed higher sensitivity and stability.

This study contrasted the impact of Tai Chi Chuan and general aerobic exercise on brain plasticity in terms of an increased grey matter volume and functional connectivity during structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), explored the advantages of Tai Chi Chuan in improving brain structure and function. Thirty-six college students were grouped into Tai Chi Chuan (Bafa Wubu of Tai Chi), general aerobic exercise (brisk walking) and control groups. Individuals were assessed with a sMRI and rs-fMRI scan before and after an 8-week training period. The VBM toolbox was used to conduct grey matter volume analyses. The CONN toolbox was used to conduct several seed-to-voxel functional connectivity analyses. We can conclude that compared with general aerobic exercise, eight weeks of Tai Chi Chuan exercise has a stronger effect on brain plasticity, which is embodied in the increase of grey matter volume in left middle occipital gyrus, left superior temporal gyrus and right middle temporal gyrus and the enhancement of functional connectivity between the left middle frontal gyrus and left superior parietal lobule. These findings demonstrate the potential and advantages of Tai Chi Chuan exercises in eliciting brain plasticity.

This paper presents a method for quickly and accurately identifying contaminant source in estuary region, characterized by decoupling solving releasing time, location and density of source problems, respectively. The method firstly gets the rough source releasing time by analyzing the typical double peak phenomenon in tidal estuary region, and then presents the rough position of source by using Lagrange tracing scheme. The rough position makes up the deficiency of priori information in traditional Genetic Algorithm (GA). And then, according to the correlation between the measured and calculated concentration at measuring point, a high resolution mass transportation and an optimization models are operated repeatedly and alternatively. In order to increase the efficiency of searching optimal parameters, the Genetic Algorithm is improved by introducing a weighting factor based on the precision improvement trend. Such an optimization model can effectively reduce the calculation burden when parameters are increased in demand This model has been successfully applied in an accident case in Quanzhou Bay of China. Simulated results confirm the model's merits in reasonably identifying relevant unknown parameters. The convergence of present model is more efficient in searching for the optimal parameters with less iteration times nearly half the traditional Genetic Algorithm. This model is high efficiency and has great practical significance in dealing with emergent water pollution in estuary and coastal areas.