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Ultrasonic guided waves (UGWs) in water-filled pipes are subject to more severe dispersion and attenuation than vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called orthogonal matching pursuit based on dispersion and multi-mode (DMOMP) was proposed, which utilizes the second-order asymptotic solution of dispersion curves and the conversion characteristics of asymmetric UGWs in the defect contact stage to reconstruct the dispersive signals and converts the time-domain dispersive signals to distance-domain non-dispersive signals by dispersion compensated time-distance mapping. The synthesized simulation results indicate that DMOMP not only exhibits higher reconstruction accuracy compared to OMP, but also reveals more accurate and stable mode recognition and localization compared to DOMP, which only considers the dispersion under perturbation and noise. In addition, the UGW testing experimental results of water-filled pipes verify the effectiveness of DMOMP, the localization accuracies of three feature signals (defct 1, defct 2 and end echo) with DMOMP are 99.10%, 98.72% and 98.36%, respectively, and the average localization accuracy of DMOMP is as high as 98.73%. [ABSTRACT FROM AUTHOR]

The increasing frequency of global drought events poses a significant threat to the stability of grassland ecosystems' functionality. The Inner Mongolian grasslands stand out as one of the world's most drought-prone regions, facing elevated drought risks compared to other biomes. An in-depth comprehension of the impact of drought on grassland ecosystems is paramount for their long-term sustainability. Using the Standardized Precipitation Evapotranspiration Index (SPEI) from 1982 to 2018, this study identified various drought events within the Inner Mongolian grasslands, encompassing moderate drought, severe drought, and extreme drought. The resistance of the vegetation to the different drought conditions, assessed through net primary productivity (NPP) as a metric (reflecting its capacity to maintain its original level during drought periods), was examined. The research findings indicated that the period from 2001 to 2018 witnessed a substantial increase in both the frequency and the extent of drought events compared to the period from 1982 to 2000, particularly concerning severe and extreme droughts. The areas most severely impacted by extreme drought were the Xilingol League and the Alxa League. From 1982–2000 to 2001–2018, under moderate drought conditions, vegetation resistance exhibited a minor decrease in the central and eastern regions but experienced a slight increase in the western region. In contrast, under severe drought conditions, the western region saw a significant decrease in vegetation resistance. Remarkably, under extreme drought conditions, the western region showed a substantial increase in vegetation resistance, while the central and eastern regions experienced a slight decrease. Across all three drought conditions, as precipitation levels declined, the resistance of the meadow–steppe–desert ecosystems demonstrated a high–low–high distribution pattern. The temperate desert steppe exhibited a minimal vulnerability to drought, boasting resistance levels exceeding 0.9. Notably, extreme drought had the most pronounced impact on the temperate meadow steppe, temperate steppe, and temperate desert steppe, particularly within the temperate meadow steppe category. Given these findings, the authorities responsible for grassland management should prioritize regions characterized by frequent drought occurrences and low drought resistance, such as Ulanqab City, the Xilingol League, and the western part of Hulun Buir City. Safeguarding steppe ecosystems is of paramount importance for stabilizing vegetation productivity and land carbon sinks, especially under the anticipated exacerbation of climate conditions in the future. [ABSTRACT FROM AUTHOR]

Grassland aboveground biomass (AGB) is an important indicator for studying the change in grassland ecological quality and carbon cycle. The rapid development of high-resolution remote sensing and unmanned aerial vehicles (UAV) provides a new opportunity for accurate estimation of grassland AGB on the plot scale. In this study, the mountain grassland was taken as the research object. Using UAV Light Detection and Ranging (LiDAR) data and multispectral satellite images, the influence of topographic correction methods on AGB estimation was compared and a series of LiDAR metrics and vegetation indices were extracted. On this basis, a comprehensive indicator, the vegetation index-height-intensity model (VHI), was proposed to estimate AGB quickly. The results show that: (1) Among the four topographic correction methods, the Teillet regression has the best effect, and can effectively improve the accuracy of AGB estimation in mountain grassland. The correlation between corrected ratio vegetation index and AGB was the highest (correlation coefficient: 0.682). (2) Among the height and intensity metrics, median height and max intensity yielded the higher accuracy in estimating AGB, with Root Mean Square Error (RMSE) of 322 g/m2 and 333 g/m2, respectively. (3) The VHI integrated spectrum and LiDAR information, and its accuracy for AGB estimation for mountain grassland, was obviously better than other indicators, with an RMSE of 272 g/m2. We also found that the accuracy of VHI in univariate models was comparable to that of complex multivariate models such as stepwise regression, support vector machine, and random forest. This study provides a new approach for estimating grassland AGB with multi-source data. As a simple and effective indicator, VHI has shown strong application potential for grassland AGB estimating in mountainous areas, and can be further applied to grassland carbon cycle research and fine management. [ABSTRACT FROM AUTHOR]

The large number of dams in the world has caused many ecological problems, especially in ecologically fragile areas. Appropriate environmental flow (e-flows) releases reduce stress on river ecosystems caused by the flow alterations of dams. As e-flow releasing is substantially a decision-making process, it is important to choose the right calculation method in many e-flow assessment frameworks and calculation methods or to select the proper e-flow result from many scenarios. In China, there are government guidelines to assist managers in choosing an e-flow method for their situations, but the technical components of these guidelines are too general and not very specific, even though the coverage of the framework of guidelines is comparatively complete. Thus, a high degree of subjectivity remains in the method selection, and managers are often confused about the different e-flow results and scenarios. A more detailed decision support evaluation (DSE) model which can quantify and compare the results from different calculation methods is therefore needed to provide a basic technical economy evaluation. Based on the relevant policies and the dam priority classification frameworks in China, we propose a DSE model based on the matter element analysis (MEA) method to complement the Chinese e-flow framework. First, the flow regime criteria from the related government guidelines for a variety of conservation objectives are summarized. Secondly, different scenarios of e-flows are calculated based on the formal government guidelines and using the same hydrological database. Finally, the DSE model for assessing the different e-flow scenarios are built and demonstrated. MEA helped to quantify and optimize the different results and reduce the uncertainty. The DSE model presented in this study can be applied to e-flow optimization for all dammed rivers in China and serve as a decision support tool for managers. [ABSTRACT FROM AUTHOR]

Policies for promoting sustainable development of dam projects in the world have always been an argument due to the negative impacts on ecosystems. How to improve the efficiency of identifying the impacts and main indicators in the environmental impact assessment (EIA) process and post environmental impact assessment (PEIA) process is a common topic. Since the rapid dam construction in the past decades, most dam projects in China have turned to operation period. In order to identify the main impacts and provide scientific bases for the decision-making process, we optimized and applied the decision-support prototype (DSP) toolkit on the cascade dam projects on the Yangtze River. Through the optimization of the module and tools, the modified DSP was more applicable for China's dams. Though the modified DSP cannot replace large-scale assessments for dam projects, it would assist the EIA and PEIA process by identifying the most relevant environmental concerns and knowledge gaps at early stages of negotiation, so that more attention can be focused on subsequent studies and evaluative processes. On the other hand, some new limitations were found in our practice. In response to the limitations, we propose several suggestions: (1) SBQ: to introduce a factor to balance the proportion evaluation result of new developed dams and existing dams in the study; (2) SBQ: to consider more about interactions between cascade dam projects and their impacts on different spatial scales; (3) EEM, extend the parameters for generating EEM from dams in areas outside of the U.S. [ABSTRACT FROM AUTHOR]

Ultrasonic guided wave (UGW) nondestructive testing suffers from poor signal interpretation accuracy caused by the coherent noise that is related to dispersion, multi-mode and mode conversion. In order to split the L(0,2) UGW from the coherent noise, split-spectrum processing with raised cosine filters of constant frequency-to-bandwidth ratio (FBR-RC-SSP) is proposed. With the advantages of time domain resolution and frequency domain split, FBR-RC-SSP is studied based on time-frequency analysis using the chirplet transform, and the effects of filter parameters on signal-to-noise ratio gain (SNRG) and defect-to-coherent noise gain (DCRG) are explained. The excellent effects of eliminating the coherent noise and improving the signal resolution in FBR-RC-SSP are reported by the validation of synthesized, simulated and experimental UGW signals, of which the average SNRG and DCRG are 22.92% and 23.71% higher than those of traditional SSP using Gaussian filters, and it has the potential to locate and characterize defects in further UGW testing research. [ABSTRACT FROM AUTHOR]

Mass concentration is a commonly used but insufficient metric to evaluate the particulate matter (PM) exposure hazard. Recent studies have declared that small particles have more serious impacts on human health than big particles given the same mass concentration. However, state-of-the-art PM sensors cannot provide explicit information of the particle size for further analysis. In this work, we adopt Sauter mean diameter (SMD) as a key metric to reflect the particle size besides the mass concentration. To measure SMD, an effective optical sensing method and a proof-of-concept prototype sensor are proposed by using dual wavelengths technology. In the proposed method, a non-linear conversion model is developed to improve the SMD measurement accuracy for aerosol samples of different particle size distributions and reflective indices based on multiple scattering channels. In the experiment of Di-Ethyl-Hexyl-Sebacate (DEHS) aerosols, the outputs of our prototype sensor demonstrated a good agreement with existing laboratory reference instruments with maximum SMD measurement error down to 7.04%. Furthermore, the simplicity, feasibility and low-cost features of this new method present great potential for distributed PM monitoring, to support sophisticated human exposure hazard assessment. [ABSTRACT FROM AUTHOR]

The pore and throat structure of tight oil reservoir cores is complex, and the resistance of oil drop to discharge from the core is very high during dynamic imbibition. Surfactant has good ability in interfacial tension reduction and wettability reversal. It can reduce oil drop discharge resistance and enhance oil recovery effectively during dynamic imbibition in tight reservoirs. Here, we first analyzed the pore throat structure and mineral composition of tight core, and then the oil drop visualization instrument was used to study the discharge behavior of oil drop during dynamic imbibition. The oil drop discharge form was analyzed, and the influence of various factors on the oil drop discharge behavior was explored, and then the dynamic imbibition performance of surfactant in tight cores was obtained. The core throat diameter was mainly distributed in 0.07–1.1 μm, and the hydrophilic mineral content in core reached 50.8%. In the case of fluid flow in fracture, the oil drop discharge from near fracture matrix was faster, and its growth rate in height and width was faster than that without external fluid flow. Within a certain range, with the increase of IFT, the rate of core imbibition increased gradually. When the IFT increased from 0.32 mN/m to 0.59 mN/m, the oil drop rapture time decreased by 66.3%. The growth rate of oil drop discharged from the top and side of the core was faster than that from the bottom surface. Furthermore, it was easier to discharge. With the core thickness reduced by half, the rapture time of oil drop was reduced by 74.7%. For tight reservoirs, hydraulic fracturing can create more fracture surfaces and reduce the size of matrix blocks, which contributes to reduce the oil drop discharge resistance during imbibition and improve the oil recovery. This study provides a basis for surfactant to improve dynamic imbibition and oil production performance of tight sandstone oil reservoir. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future. [ABSTRACT FROM AUTHOR]