Search Results (9,955)

The InVEST model, with its ability to perform spatial visualization and quantification, is an important tool for mapping ecosystem services. However, the spatial accuracy and simulating performance of the model are deeply influenced by the land use parameter, which often relies on the accuracy of land use/cover data. To address this issue, we propose a novel method for optimizing the land use parameter of the InVEST model based on the vegetation–impervious surface–soil (V–I–S) model and a machine learning algorithm. The optimized model is called Sub-InVEST, and it improves the performance of assessing ecosystem services on a sub-pixel scale. The conceptual steps are (i) extracting the V–I–S fraction of remote sensing images based on the spectral unmixing method; (ii) determining the mapping relationship of the V–I–S fraction between land use/cover type using a machine learning algorithm and field observation data; (iii) inputting the V–I–S fraction into the original model instead of the land use/cover parameter of the InVEST model. To evaluate the performance and spatial accuracy of the Sub-InVEST model, we employed the habitat quality module of InVEST and multi-source remote sensing data, which were applied to acquire Sub-InVEST and estimate the habitat quality of central Guangzhou city from 2000 to 2020 with the help of the LSMA and ISODATA methods. The experimental results showed that the Sub-InVEST model is robust in assessing ecosystem services in sets of complex ground scenes. The spatial distribution of the habitat quality of both models revealed a consistent increasing trend from the southwest to the northeast. Meanwhile, linear regression analyses observed a robust correlation and consistent linear trends, with R² values of 0.41, 0.35, 0.42, 0.39, and 0.47 for the years 2000, 2005, 2010, 2015, and 2020, respectively. Compared with the original model, Sub-InVEST had a more favorable performance in estimating habitat quality in central Guangzhou. The spatial depictions and numerical distribution of the results of the Sub-InVSET model manifest greater detail and better concordance with remote sensing imagery and show a more seamless density curve and a substantially enhanced probability distribution across interval ranges. Full article

Soil erosion within agricultural landscapes has significant environmental and economic impacts and is strongly driven by reduced residue cover in agricultural fields. Large-area soil erosion models such as the Daily Erosion Project are important tools for understanding the patterns of soil erosion, but they rely on the accurate estimation of crop residue cover over large regions to infer the tillage practices, an erosion model input. Remote sensing analyses are becoming accepted as a reliable way to estimate crop residue cover, but most use localized training datasets that may not scale well outside small study areas. An alternative source of training data may be commonly conducted tillage surveys that capture information via rapid “windshield” surveys. In this study, we utilized the Google Earth Engine to assess the utility of three crop residue survey types (windshield tillage surveys, windshield binned residue surveys, and photo analysis surveys) and one synthetic survey (retroactively binned photo analysis data) as sources of training data for crop residue cover regressions. We found that neither windshield-based survey method was able to produce reliable regressions but that they can produce reasonable distinctions between low-residue and high-residue fields. On the other hand, both photo analysis and retroactively binned photo analysis survey data were able to produce reliable regressions with r² values of 0.57 and 0.56, respectively. Overall, this study demonstrates that photo analysis surveys are the most reliable dataset to use when creating crop residue cover models, but we also acknowledge that these surveys are expensive to conduct and suggest some ways these surveys could be made more efficient in the future. Full article

The transport sector heavily relies on the use of fossil fuels, which are causing major environmental concerns. Solutions relying on the direct or indirect use of electricity through e-fuel production are emerging to power the transport sector. To ensure environmental benefits are achieved over this transition, an accurate estimation of the impact of the use of electricity is needed. This requires a high temporal resolution to capture the high variability of electricity. This paper presents a previously unseen temporal analysis of the carbon intensity of e-fuels using grid electricity in countries that are members of the European Network of Transmission System Operators (ENTSO-E). It also provides an estimation of the potential load factor for producing low-carbon e-fuels according to the European Union legislative framework. This was achieved by building on top of the existing EcoDynElec tool to develop EcoDynElec_xr, a python tool enabling—with an hourly time resolution—the calculation, visualisation, and analysis of the historical time-series of electricity mixing from the ENTSO-E. The results highlight that, in 2023, very few European countries were reaching low carbon intensity for electricity that enables the use of grid electricity for the production of green electrolytic hydrogen. The methodological assumptions consider the consumption of the electricity mix instead of the production mix, and the considered time step is of paramount importance and drastically impacts the potential load factor of green hydrogen production. The developed tools are released under an open-source license to ensure transparency, result reproducibility, and reuse regarding newer data for other territories or for other purposes. Full article

When there are time division multiple access (TDMA) signals with large bandwidth, waveform aliasing, and fast frequency-hopping in space, current methods have difficulty achieving the accurate localization of radiation sources and signal-sorting from multiple network stations. To solve the above problems, a distributed passive positioning and network stations sorting method for broadband frequency-hopping signals based on two-level parameter estimation and joint clustering is proposed in this paper. Firstly, a two-stage filtering structure is designed to achieve control filtering for each frequency point. After narrowing down the parameter estimation range through adaptive threshold detection, the time difference of arrival (TDOA) and the velocity difference of arrival (VDOA) can be obtained via coherent accumulating based on the cross ambiguity function (CAF). Then, a multi-station positioning method based on the TDOA/VDOA is used to estimate the position of the target. Finally, the distributed joint eigenvectors of the multi-stations are constructed, and the signals belonging to different network stations are effectively classified using the improved K-means method. Numerical simulations indicate that the proposed method has a better positioning and sorting effect in low signal-to-noise (SNR) and low snapshot conditions compared with current methods. Full article

The quality of sediments in the mixing zone of river freshwater and marine saline water as an important geochemical barrier for potentially toxic elements (PTEs) remains poorly understood. This study aims to analyze the current pollution with PTEs and associated ecological risks in sediments of the Don River delta and the surrounding area of the Taganrog Bay of the Sea of Azov (Russia). The PTE content was determined in fifty-four collected samples using the WDXRF and assessed using geochemical and ecotoxicological indicators. The source of Cr, Mn, Ni and Pb is mainly river runoff, and Cu, Zn and Cd are from a variety of anthropogenic sources. As shown by the assessment of the geoaccumulation index (Igeo), single pollution index (PI) and contamination factor (CF), these elements are the priority pollutants. According to these estimates, high and very high contamination of sediments in the estuarine zone of the Don River with Cd and Pb was detected in 72–94% and 2–57% of samples, respectively. However, environmental risks are determined almost exclusively by the level of Cd. Total contamination as assessed by the Nemerow pollution index (NPI), modified degree of contamination (mC_d) and metal pollution index (MPI) is of concern in 83–98% of the samples studied. The most heavily polluted sediments are in the vicinity of residential areas of the Taganrog Bay. Despite the lower average pollution levels of deltaic sediments, freshwater biota are exposed to higher potential toxic risks of adverse effects by PTE, particularly from Ni and Pb. Thus, the complex hydrological regime and uneven anthropogenic impact predetermine the geochemical state of the sediments of the estuarine zone of the Don River. Full article

Since the legalization of insect protein in pet food, a variety of products incorporating this ingredient have emerged on the market. Although edible insects are acknowledged for high protein content, chitin can also elevate the quantity of indigestible carbohydrates. The objective of this study was to evaluate the nutritional adequacy of fourteen complete dog foods containing edible insects in accordance with the FEDIAF nutritional guidelines. Due to the use of insects as the predominant animal component in all diets, analyses of dietary fiber fractions were carried out to estimate the content of indigestible carbohydrates. The analyses included the assessment of chemical composition, calcium, and phosphorus levels and metabolizable energy. The findings were then compared with the data provided by the manufacturers. All diets were found to meet the minimum recommended levels from the FEDIAF nutritional guidelines for protein (18.0 g/100 g DM) and fat (5.5 g/100 g DM). However, discrepancies were noted between the label data and analysis results. The results for the dietary fiber fraction differed from the crude fiber content, which is consistent with the imprecision inherent to the crude fiber determination method. In one food, there was a discrepancy of up to 19.21 g between the NDF fraction and the crude fiber content. Calcium levels were inadequate in two foods, and furthermore, twelve foods exhibited an abnormal calcium/phosphorus ratio. These findings indicate that while edible insects can be a valuable protein source, their inclusion may lead to increased indigestible carbohydrates, potentially causing digestive issues and gastric discomfort in dogs. Full article

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R² of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies. Full article

Accurate and reliable estimation of the state of health (SOH) of lithium-ion batteries is crucial for ensuring safety and preventing potential failures of power sources in electric vehicles. However, current data-driven SOH estimation methods face challenges related to adaptiveness and interpretability. This paper investigates an adaptive and explainable battery SOH estimation approach using the eXtreme Gradient Boosting (XGBoost) model. First, several battery health features extracted from various charging and relaxation processes are identified, and their correlation with battery aging is analyzed. Then, a SOH estimation method based on the XGBoost algorithm is established, and the model’s hyper-parameters are tuned using the Bayesian optimization algorithm (BOA) to enhance the adaptiveness of the proposed estimation model. Additionally, the Tree SHapley Additive exPlanation (TreeSHAP) technique is employed to analyze the explainability of the estimation model and reveal the influence of different features on SOH evaluation. Experiments involving two types of batteries under various aging conditions are conducted to obtain battery cycling aging data for model training and validation. The quantitative results demonstrate that the proposed method achieves an estimation accuracy with a mean absolute error of less than 2.7% and a root mean squared error of less than 3.2%. Moreover, the proposed method shows superior estimation accuracy and performance compared to existing machine learning models. Full article

Background/Objectives: Vitamin and/or mineral supplements are designed to correct micronutrient deficiencies or maintain adequate intake. However, evidence suggests the indiscriminate use of these products, particularly among populations that already meet their micronutrient requirements through diet. This study aims to estimate the prevalence of vitamin and/or mineral supplement use and assess the dietary intake of micronutrients among users and non-users in the Brazilian adult and elderly populations. Methods: The prevalence of vitamin and/or mineral supplement use was estimated from a sample of 37,364 individuals who participated in the Brazilian National Food Survey, a module of the 2017–2018 Household Budget Survey. The average dietary intake of micronutrients—including calcium, magnesium, phosphorus, iron, copper, zinc, vitamin A, thiamine, riboflavin, niacin, cobalamin, pyridoxine, vitamin D, vitamin E, vitamin C, and folate—was calculated for both users and non-users of these supplements, based on 24 h dietary recalls collected during the survey. Analyses of dietary intake were stratified by sex and age group. Results: The estimated overall prevalence of supplement use was 16.0% (95% CI: 15.4–16.6), with a higher prevalence among women (19.5% [95% CI: 18.7–20.5]) and the elderly (27.9% [95% CI: 26.4–29.4]). Women who used vitamin and/or mineral supplements showed higher average intakes for a greater number of dietary micronutrients compared to non-users. Conclusions: The findings from the analysis of average micronutrient intake from food sources, particularly among women and elderly women who used supplements, support the paradox of the “inverse supplement hypothesis”, which suggests that individuals who use dietary supplements are often those with the least need for them. Full article

The microphysical characteristics of precipitation and their differences among four typical weather systems over Hainan Island were investigated via multi-source observations from 2019 to 2023. We find that the cold fronts (CFs) have the greatest concentration of small raindrops, with a more substantial raindrop condensation process. The subtropical highs (SHs), with primarily deep convection and more prominent evaporation at low levels, lead to greater medium-to-large raindrops (diameters > 1 mm). Tropical cyclones (TCs) are characterized mainly by raindrop condensation and breakup, resulting in high concentrations of small raindrops and low concentrations of large raindrops. The trough of low pressures (TLPs) produces the lowest concentration of small raindrops because of evaporation processes. The convective clusters of the SHs are between maritime-like and continental-like convective clusters, and those of the other three types of weather systems are closer to maritime-like convective clusters. The relationships between the shape parameter (μ) and the slope parameter (Λ), as well as between the reflectivity factors (Z) and the rain rates (R), were established for the four weather systems. These results could improve the accuracy of radar quantitative precipitation estimation and the microphysical parameterizations of numerical models for Hainan Island. Full article

The impact of urban form on carbon emissions has become a crucial issue for sustainable socioeconomic development and the advancement of low-carbon cities. Transportation is a significant source of urban carbon emissions, highlighting the need for comprehensive research to aid China in achieving its carbon peak and neutrality goals. Currently, there is a lack of quantitative studies exploring the effects of urban form on transportation-related carbon emissions. This paper seeks to quantify the effect of urban form on the spatial and temporal patterns of transportation carbon emissions, utilizing panel data from 27 cities in the Yangtze River Delta (YRD) region of China, covering the years 2000 to 2020. First, CO₂ emissions from transportation are estimated following IPCC guidelines, with Moran’s I utilized to analyze spatial autocorrelation. Next, urban form indicators are quantified based on landscape ecology theory. Finally, econometric models are employed for regression analysis of the panel data. The findings reveal that urban complexity, compactness, and expansion influence transportation carbon emissions to varying degrees, with urban expansion and complexity associated with increased emissions, while compactness contributes to their reduction. This study offers theoretical support and a scientific basis for low-carbon urban spatial planning and development, underscoring the importance of urban form in emissions reduction strategies. Full article

This study investigates the effect of geological field measurement (offset), global positioning system (GPS), and interferometric synthetic aperture radar (InSAR) data on the estimation of the co-seismic earthquake displacements of the 2002 Denali earthquake. The analysis is conducted using stochastic source modeling. Uncertainties associated with each dataset limit their effectiveness in source model selection and raise questions about the adequate number of datasets and their type for reliable source estimation. To address these questions, stochastic source models with heterogeneous earthquake slip distributions are synthesized using the von Kármán wavenumber spectrum and statistical scaling relationships. The surface displacements of the generated stochastic sources are obtained using the Okada method. The surface displacements are compared with the available datasets (i.e., offset, GPS, and InSAR) individually and in an integrated form. The results indicate that the performance of stochastic source generation can be significantly improved in the case of using GPS data and in the integrated case. Overall, based on the case study of the 2002 Denali earthquake, the combined use of all available datasets increases the robustness of the stochastic source modeling method in characterizing surface displacement. However, GPS data contribute more than InSAR and offset data in producing reliable source models. Full article

This study employed an integrated field monitoring approach, combining chemical analysis and ecotoxicity testing of multiple environmental matrices—water, sediment, and sediment elutriates—to comprehensively assess the environmental health of the Andong watershed, located near a Zn smelter and mining area. The primary objectives were to evaluate the extent of metal contamination, identify key toxicants contributing to ecological degradation, and trace the sources of these pollutants. Our findings revealed severe metal contamination and significant ecotoxicological effects both in proximity to and downstream from industrial sites. Specifically, Cd, Zn, and Pb were strongly linked to the smelter, while Hg, Ni, Cu, and As were predominantly associated with mining activities in the tributaries. To further assess toxicity of field-collected sediment and their elutriates, a logistic regression analysis was employed to estimate benchmark values for distinguishing between toxic and non-toxic samples, using the sum of toxic units for sediment elutriates and the mean probable effect level (PEL) quotient for sediment toxicity. These models demonstrated greater predictive accuracy than conventional benchmarks for determining toxicity thresholds. Our results highlight that integrating chemical and ecotoxicological monitoring with site-specific concentration–response relationships enhances the precision of ecological risk assessments, facilitating more accurate identification of key toxicants driving mixture toxicity in complex, pollution-impacted aquatic ecosystems. Full article

Fault location estimation in transmission lines is critical for power system reliability. Various methods have been developed for this purpose, among which transient frequency spectrum analysis (TFSA) stands out as a recent method based on travelling wave (TW) theory. TFSA determines the fault location by analyzing the frequency spectrum of transient currents and/or voltages at the instant of the fault, offering advantages such as independence from fault impedance and the ability to locate faults with one-side measurements. Despite its success in fault location, TFSA has several considerations that warrant detailed investigation. This study explores the effects of source inductance, series compensation, fault arc, and current transformer (CT) characteristics on transient frequencies. Additionally, the impact of noise on TFSA results is examined. The new proposed source inductance compensation method can reduce the error of 6.55% to 0.88%, where the same error can be reduced to 3.45% with the compensation method given in previous study. Strategies to enhance accuracy are discussed and compared to previous studies, including a proposed detection approach providing appropriate data size and precise wave propagation speed calculations. These findings contribute to a deeper understanding of TFSA’s limitations and inform practical improvements for fault location accuracy in power transmission systems. Full article

Decarbonization of the built environment by electrifying energy systems and decarbonizing the electrical grid coupled with the digitization of these systems is a central strategy implemented by the European Commission (EC) to meet carbon reduction policies. The proliferation of technologies such as renewable energy sources (RES) and demand-side management (DSM) systems can be improved by using digital twins to predict and optimize their integration with existing systems. Digital twins in the built environment have been used for multiple purposes, such as predicting the performance of a system before its inception or optimizing its operation during use. To this end, a novel application of a combination of these technologies towards optimized DSM is peer-to-peer (P2P) energy trading, which can improve the local use of RES in the built environment. This paper investigates the potential of P2P energy trading in optimizing local RES of a remote island, Inishmore, Republic of Ireland, using a combination of data-driven and predictive digital twins towards the island’s journey to net zero. Data-driven digital twins are used to evaluate the current energy use at the pilot site. Predictive digital twins are applied to estimate the impact of applying P2P in the future and its influence on RES consumption at the pilot site. The findings show that in scenarios with limited RES coverage, P2P can significantly increase the local consumption of excess RES energy, reducing the risk of transmission or curtailment losses. However, P2P is limited in scenarios with widespread RES installation without storage or behavioral change to shift energy loads. Full article