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To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.

Quantifying the relative contributions of local emissions and regional transport is critical for urban air quality management. Chemical transport models (CTMs) are widely applied for source apportionment, but they require detailed emission inventories, extensive input data, and substantial computational resources, which limit their operational use. In contrast, urban monitoring networks provide continuous and readily available observations. This study develops an observation-based framework that estimates regional contribution ratios (RCs) from inter-station concentration variability, quantified by the coefficient of variation (CV), using WRF–CAMx results as a reference. Using Linyi as the primary case, with Xi’an and Beijing for comparison, concentration-stratified regression was applied to establish CV–RC relationships. Results show a consistent nonlinear relationship between CV and RC, with coefficients of determination (R2) up to 0.86 for PM10 (daily), 0.81 for NO2 (hourly), and 0.78–0.79 for O3. CV decreases markedly with increasing concentration; for PM2.5, values decline from ~0.17–0.18 to 0.05–0.06 (≈65–70%), indicating enhanced spatial homogeneity under regional influence. The relationship is most stable within a 10–15 km spatial scale. Application-based evaluation for January 2022 shows moderate agreement between estimated and modeled RC (R = 0.55–0.65), reflecting pollutant-dependent uncertainties, partly associated with biases in the model-derived reference RC. These results demonstrate that inter-station concentration variability provides a first-order, computationally efficient indicator of the balance between local emissions and regional transport.

In modern power systems, the extensive integration of renewable energy sources leads to a reduction in system inertia, thereby affecting grid stability. This study proposes a novel method for estimating grid inertia, which, under quasi-steady state conditions, effectively incorporates load contribution by combining class noise signals collected by PMUs (Phasor Measurement Units) with the Forgetting Factor Unscented Kalman Filter algorithm (FF-UKF). The research initially analyzes the characteristics of various inertia sources in power systems dominated by renewable energy. Building on this, we propose a grid node equivalent inertia estimation model based on node equivalent frequency response, developing an FF-UKF-based algorithm that allows for the continuous dynamic estimation of inertia at various grid nodes. Simulation experiments conducted within the IEEE 39 bus system validate the method’s effectiveness and demonstrate the spatiotemporal distribution of grid inertia under varying levels of renewable energy penetration. This study is significant for understanding and enhancing frequency control and stability in power systems, providing key technical support for the modernization of the power system and the efficient integration of renewable energy.

Fine particulate matter (PM2.5) has a serious impact on human health. Forecasting PM2.5 levels and analyzing the pollution sources of PM2.5 are of great significance. In this study, the Lagrangian particle dispersion (LPD) model was developed by combining the FLEXPART model and the Bayesian inventory optimization method. The LPD model has the capacity for real-time forecasting and determination of pollution sources of PM2.5, which refers to the contribution ratio and spatial distribution of each type of pollution (industry, power, residential, and transportation). In this study, we applied the LPD model to the Beijing-Tianjin-Hebei (BTH) region to optimize the a priori PM2.5 emission inventory estimates during 15–20 March 2018. The results show that (1) the a priori estimates have a certain degree of overestimation compared with the a posteriori flux of PM2.5 for most areas of BTH; (2) after optimization, the correlation coefficient (R) between the forecasted and observed PM2.5 concentration increased by an average of approximately 10%, the root mean square error (RMSE) decreased by 30%, and the IOA (index of agreement) index increased by 16% at four observation sites (Aotizhongxin_Beijing, Beichenkejiyuanqu_Tianjin, Dahuoquan_Xintai, and Renmingongyuan_Zhangjiakou); and (3) the main sources of pollution at the four sites mainly originated from industrial and residential emissions, while power factory and transportation pollution accounted for only a small proportion. The concentration of PM2.5 forecasts and pollution sources in each type of analysis can be used as corresponding reference information for environmental governance and protection of public health.

Excessive nitrogen loads and subsequent eutrophication risk have led to a series of critical water quality problems in Chinese watersheds. To address this issue, a modeling approach is useful for quantifying nitrogen sources, assessing source apportionment, and guiding management responses. In this study, we modeled the main hydrochemical processes of the Lian River watershed located in the south of China using the Regional Nutrient Management (ReNuMa) model, a model derived from the Generalized Watershed Loading Function (GWLF) model and incorporating Net Anthropogenic Nitrogen Inputs (NANI) to estimate runoff nitrogen concentrations. An informal Bayesian method, the Generalized Likelihood Uncertainty Estimation (GLUE) procedure, was applied for model calibration and uncertainty analysis. The resulting modeled monthly total nitrogen fluxes have high Nash-Sutcliff coefficients (>0.85) for the calibration (2005–2009) and verification (2003, 2004 and 2010) periods, representing an acceptable goodness-of-fit. The model outputs were further processed using multivariate statistical analysis to determine latent rules of nitrogen source apportionment under different circumstances, including different water regimes, seasonal patterns, and loading levels. The main nitrogen contributions in different natural and management-driven conditions have been identified, and appear to be significant for supporting decision-making priorities. We find that the ReNuMa model, with its Bayesian procedure and the linkage of subsequent multivariate statistical analysis, represents a useful approach with applicability within China and a great potential to be extended elsewhere.

Purpose of Review Our review provides an overview of forest attributes measurable by forest inventory that may support the integration of non-provisioning ecosystem services (ES) and biodiversity into forest planning. The review identifies appropriate forest attributes to quantify the opportunity for recreation, biodiversity promotion and carbon storage, and describes new criteria that future forest inventories may include. As a source of information, we analyse recent papers on forest inventory and ES to show if and how they address these criteria. We further discuss how mapping ES could benefit from such new criteria and conclude with three case studies illustrating the importance of selected criteria delivered by forest inventory. Recent Findings Recent studies on forest inventory focus mainly on carbon storage and biodiversity promotion, while very few studies address the opportunity of recreation. Field sampling still dominates the data collection, despite the fact that airborne laser scanning (ALS) has much improved the precision of large-scale estimates of the level of forest ES provision. However, recent inventory studies have hardly addressed criteria such as visible distance in stands, presence of open water bodies and soil damages (important for the opportunity of recreation) and naturalness (here understood as the similarity of the forest to its natural state) and habitat trees and natural clearings (important for biodiversity promotion). The problem of quantifying carbon stock changes with appropriate precision has not been addressed. In addition, the reviewed studies have hardly explored the potential of inventory information to support mapping of the demand for ES. Summary We identify challenges with estimating a number of criteria associated with rare events, relevant for both the opportunity of recreation and biodiversity promotion. These include deadwood, rare species and habitat trees. Such rare events require innovative inventory technology, such as point-transect sampling or ALS. The ALS technology needs relatively open canopies, to achieve reliable estimates for deadwood or understorey vegetation. For the opportunity of recreation, the diversity among forest stands (possibly quantified by geoinformatics) and information on the presence of open water bodies (provided by RADAR, ALS data or use of existing maps) may be important. Naturalness is a crucial criterion for native biodiversity promotion but hard to quantify and assess until now. Tree species identification would be crucial for this criterion, which is still a challenge for remote sensing techniques. Estimating carbon storage may build on biomass estimates from terrestrial samples or on remotely sensed data, but major problems exist with the precision of estimates for carbon stock changes. Recent approaches for mapping the supply side of forest ES are promising, while providing so far uncommon structural information by revised inventory concepts could be helpful also for mapping the demand for ES. We conclude that future studies must find holistic inventory management systems to couple various inventory technologies in support of the integration of non-provisioning ES and biodiversity into forest planning.

We define animal husbandry as prey conservation. Conservation is rare among extant hunters and only likely to occur when prey are highly valued, private goods. The long-term discounted deferred returns from husbandry must also be greater than the short-term returns from hunting. We compare the returns from hunting and husbanding strategies as a function of prey body size. Returns from husbanding are estimated using a maximum sustainable yield (MSY) model. Following Charnov (1993), allometric analyses show that the MSY is nearly independent of prey body size. The opportunity costs of husbanding are measured as prey standing biomass times the discount rate. Since standing biomass scales positively with body size, the opportunity costs of husbanding are greater for larger animals. An evolutionary discount rate is estimated following Rogers (1994) to be between 2.4% and 6%. Using these values, the prey body size for which hunting and meat-only husbanding provide the same return is approximately 40kg. Animals greater than 40kg are predicted to be hunted.

This paper describes how estimates of individual charitable giving are derived from two major continuous surveys: the Family Expenditure Survey and the Individual Giving Survey. It explores the reasons for and the significance of the differences between the two estimates. Conclusions are drawn on the relative merits and demerits of the two survey datasets, and the circumstances in which it might be appropriate to use each of them. L'article décrit deux enquêtes majeures, la Family Expenditure Survey (Enquête sur les Dépenses Familiales) et la Individual Giving Survey (Enquête sur les Dons Individuels) sur lesquelles se basent les estimations des dons individuels aux oeuvres de bienfaisance. Il analyse les raisons et la signification de la différence entre les deux estimations et tire des conclusions quant aux avantages et aux inconvénients des deux sources de données et aux circonstances justifiant l'utilisation soit de l'une, soit de l'autre. Der vorliegende Artikel stellt dar, wie Schätzungen zum individuellen Spendenaufkommen aus zwei größeren kontinuierlichen Erhebungen abgeleitet werden können. Es werden die Gründe für Unterschiede zwischen verschiedenen Schätzverfahren dargestellt. Der Artikel kommt zu Schlußfolgerungen über die relativen Vor- und Nachteile der beiden Datensätze und über die Bedingungen, unter denen sie jeweils anwendbar sind. En este escrito se describe cómo los cálculos de donativos benéficos individuales provienen de dos importantes encuestas realizadas continuamente: la Encuesta Sobre Gastos Familiares y la Encuesta Sobre Donaciones Individuales. En el escrito se examinan los motivos y la trascendencia de las diferencias entre los dos cálculos. Se sacan conclusiones de los méritos y deméritos relativos de los conjuntos de datos de ambas encuestas, y de la circunstancias en las que pudiera ser apropiado utilizar cada una de ellas.