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This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today's modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly. [ABSTRACT FROM AUTHOR]

The rapid spread of capitalism in rural areas has facilitated rural land commodification (RLC). While some scholars have studied RLC, few have analyzed its spatial characteristics. Taking Shijiazhuang city as a study area, this paper applies Moran's I method and spatial regression models to analyze township-scale RLC patterns and driving factors. The study investigates four common pathways of RLC: production-oriented farmland, tourism-oriented farmland, rural homesteads, and construction land commodification which are predominantly found in urban fringe areas. The distribution of RLC demonstrates positive spatial autocorrelation, characterized by spatial aggregation and polarization. Population, economic level, agriculture, and location conditions are identified as key drivers, and their specific mechanisms vary across development pathways. Future efforts should focus on ensuring balanced and coordinated RLC development in accordance with regional conditions and capacity, while also addressing the implications arising from the coexistence of RLC with rural aging and poverty. [ABSTRACT FROM AUTHOR]

Geochemical lithogenes have been successfully applied as an innovative concept in the field of composition classification and source traceability of geological materials recently. This paper introduces the background of the development of geochemical genes and the construction and application of LG01 and LG03 lithogenes. Based on LG01 and LG03, the LG_CR classification and provenance are applied and verified on a weathering profile, ten gully sedimentary profiles and regional stream sediments in the Wanquan area of Zhangjiakou city, Hebei province, China. The geochemical lithology of the weathering profile shows a gradual variation from basic-like in the bottom rock to acidic-like at the upper soils compositionally with heterogeneity. The classification results on 10 sedimentary gully profiles (each with five samples) indicate that soils at the bottom of the gully system are dominated with 11 types of LG_CR materials, while the top materials are made up of 21 types, reflecting the mixing of the upstream soils. The results of stream sediments from a regional geochemical survey with a scale of 1:200,000 in this area illustrate that the classification results of LG_CR on stream sediments are basically consistent with the petrological results derived from regional geological mapping. Therefore, LG_CR can be used not only as an effective tool for classification and traceability of geological materials but also has great potential in lithological mapping in petrological-overburdened areas. [ABSTRACT FROM AUTHOR]

The study of the characteristics and variations of cloud condensation nuclei (CCN) plays an important role in understanding the effects of aerosol–cloud interactions. This paper selected observation data in a city region of Shijiazhuang in North China from 2005 to 2007, along with the corresponding MERRA-2 and ERA5 data, to analyze the characteristics of CCN, sort the factors affecting the diurnal variation of CCN number concentration ( N CCN ) according to their importance, and build the relationship between N CCN and supersaturation ( SS ) in the heavily polluted region. The results show that there was a bimodal distribution of a daily time series for N CCN in Shijiazhuang, China. By calculating the correlation between CCN and pollutants observed in winter 2007, we identified that the dominant factor for peaks of N CCN is SO 2 in the morning but NO 2 in the evening. We also ranked the factors affecting the diurnal variation of N CCN by using observation and reanalysis data and found that the concentration of pollutants is the greatest impact factor in summer, but the atmospheric stability is the greatest factor in winter. Finally, we determined the relationship between N CCN and SS according to the Twomey formula ( N CCN = cSS k ) and found there was a reasonable value range (i.e., 0.5~0.7) for the parameter k in East and North China. Specifically, it is more reasonable for k to be 0.5 in summer and 0.7 in winter. [ABSTRACT FROM AUTHOR]

The accuracy of doubly fed induction generator (DFIG) models and parameters plays an important role in power system operation. This paper proposes a parameter identification method based on the hybrid genetic algorithm for the control system of DFIG converters. In the improved genetic algorithm, the generation gap value and immune strategy are adopted, and a strategy of "individual identification, elite retention, and overall identification" is proposed. The DFIG operation data information used for parameter identification considers the loss of rotor current, stator current, grid-side voltage, stator voltage, and rotor voltage. The operating data of a wind farm in Zhangjiakou, North China, were used as a test case to verify the effectiveness of the proposed parameter identification method for the Maximum Power Point Tracking (MPPT), constant speed, and constant power operation conditions of the wind turbine. [ABSTRACT FROM AUTHOR]

The Hutuo River Drinking Water Source Area is an important water source of Shijiazhuang (North China Plain). Knowing the characteristics of groundwater chemistry/quality is essential for the protection and management of water resources. However, there are few studies focused on the groundwater chemistry evolution over the drinking water area. In this study, total of 160 groundwater samples were collected in November 2021, and the spatial distribution of groundwater chemistry and related controlling factors were analyzed using hydrological and multivariate analysis. The entropy-weighted water quality index (EWQI) was introduced to assess the groundwater quality. The results show that the hydrogeochemical types of groundwater are Ca-HCO3 (78.1%), mixed Ca-Mg-Cl (20%), and Ca-Cl (1.9%) in the area. Graphical and binary diagrams indicate that groundwater hydrochemistry is mainly controlled by water–rock interaction (i.e., rock weathering, mineral dissolution, and ion exchange). Five principal components separated from the principal component analysis represent the rock–water interaction and agricultural return, redox environment, geogenic sources, the utilization of agricultural fertilizer, the weathering of aluminum silicates, and dissolution of carbonates, respectively. More than 70% of the samples are not recommended for irrigation due to the presence of high salt content in groundwater. EWQI assessment demonstrates that the quality of the groundwater is good. The outcomes of this study are significant for understanding the geochemical status of the groundwater in the Hutuo River Drinking Water Source Area, and helping policymakers to protect and manage the groundwater. [ABSTRACT FROM AUTHOR]

Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Shijiazhuang, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application. [ABSTRACT FROM AUTHOR]

Water security in the ecotone between semi-humid and semi-arid regions (EHA) is very vulnerable and sensitive to climate change and human interferences. Urban turf irrigation is a primary consumer of urban water resources in the EHA, which places huge pressures on water security by substantial irrigated water use due to the expansion of urban turf planting. Based on a 2-year (2020–2021) turf experiment in Zhangjiakou City, a typical water-deficit city in the EHA of northern China, the water budget for turf was measured and analyzed. Furthermore, the Root Zone Water Quality Model (RZWQM2) was employed to evaluate the optimal irrigation scheme for turf. The results showed that the average volumetric water content in the 0–40 cm soil layer was maintained above 23% in 2020–2021. The evapotranspiration in growth period of turf accounted for more than 70% of the annual evapotranspiration, and the deep seepage in turf soil accounted for 49.67% and 60.28% of the total precipitation and irrigation in 2020 and 2021, respectively, during the vigorous growth period of the turf from May to September. The calibrated RZWQM2 showed a robust ability to simulate the water changes in turf. The d-values (consistency index) between the simulated and observed volumetric water contents and evapotranspiration were both greater than 0.90. In the aspects of irrigation scenarios, the T60%-12 scenario (TA-B, where A is 100%, 80%, 60% or 40% of the total irrigation amount and B is the number of irrigation events corresponding to A) was determined as the best irrigation schedule in our study area because of lower evapotranspiration, seepage and higher turf soil water storage under this irrigation scenario, also resulting from the comparison of different irrigation scenarios using the entropy-weight-TOPSIS method. In such an optimal scenario, T60%-12 irrigation treatment reduced the irrigated water requirement of turf by 40% (142.06 mm) and the seepage amount by 28.07% (39.05 mm), and had the lowest negative impacts on the turf growth. [ABSTRACT FROM AUTHOR]

Cities located in the transitional zone between Taihang Mountains and North China plain run high flood risk in recent years, especially urban waterlogging risk. In this paper, we take Shijiazhuang, which is located in this transitional zone, as the study area and proposed a new flood risk assessment model for this specific geographical environment. Flood risk assessment indicator factors are established by using the digital elevation model (DEM), along with land cover, economic, population, and precipitation data. A min-max normalization method is used to normalize the indices. An analytic hierarchy process (AHP) method is used to determine the weight of each normalized index and the geographic information system (GIS) spatial analysis tool is adopted for calculating the risk map of flood disaster in Shijiazhuang. This risk map is consistent with the reports released by Hebei Provincial Water Conservancy Bureau and can provide reference for flood risk management. [ABSTRACT FROM AUTHOR]

Glass-ceramics acquired from the melting of rocks have a vast application marketplace. In this study, an olivine basalt rock from Zhangjiakou in China was selected as a raw material to prepare basalt glass-ceramics, and the crystallization kinetics of olivine basalt glass was investigated using differential thermal analysis. Polarizing microscope and X-ray diffraction (XRD) analysis results revealed that the main mineral compositions of olivine basalt are plagioclase, pyroxene, olivine, and iron oxide(s). Three crystallization peaks were observed in the DSC curve of the olivine basalt glass. The Avrami exponent (n), apparent activation energies for the crystallization, and glass transition of basalt glass were determined using the Owaza method based on data obtained from isothermal measurements. The crystallization activation energies (E) of the three crystallization peaks of olivine basalt glass were 314.20 kJ/mol, 1232.49 kJ/mol, and 696.89 kJ/mol, respectively. In addition to this, the crystal growth index indicated that the crystallization mode in the olivine basalt glass was surface crystallization. The crystallization phases and microstructure of the olivine basalt glass heated at 860 °C, 1100 °C, and 1180 °C were also studied. The conclusions obtained offer some useful information for the preparation of basalt glass-ceramics from olivine basalt rocks. [ABSTRACT FROM AUTHOR]

Spurred by the degraded forest in the 2022 Chongli Winter Olympic Games area, the Chinese government initiated a national program for mountain forest rehabilitation. We developed a method to assess the quality of mountain forests using an index system composed of stand structure, site conditions, and landscape aesthetics at three criteria levels. The method involves index weights determined by the analytical hierarchy process (AHP) and entropy method. The results show that landscape aesthetics was the most important measure for the criterion layer. Slope aspect and naturalness were the most and second-most important indices, respectively, for the alternative layer. The quality of the mountain forest in the Chongli area was divided into four grades. The area had 7.8% with high quality, 46.7% with medium quality, 36.6% with low quality, and 8.9% with inferior quality. In total 76.6% of the damaged forest were distributed on sloping and steep sloping ground at 1700 to 2050 m altitude, and Betula platyphylla Sukaczev and Larix gmelinii var. principis-rupprechtii (Mayr) Pilg. were the predominating trees. The damaged forest was divided into over-dense, over-sparse, degraded, inappropriate tree species, and inferior landscape forest. For different types of damaged forest, corresponding modification measures were proposed. The methods developed in this study can be used for rehabilitation projects to improve the quality of degraded forests in mountainous temperate areas. [ABSTRACT FROM AUTHOR]

Shield tunneling under rivers often requires monitoring riverbed deformations in near real-time. However, it is challenging to measure riverbed deformation with conventional survey techniques. This study introduces a comprehensive method that uses the Global Positioning System (GPS) of the USA and the BeiDou navigation satellite system (BeiDou) of China to monitor riverbed deformation during the construction of twin tunnels beneath the Hutuo River in Shijiazhuang, China. A semi-permanent GPS network with one base station outside the river and six rover stations within the river was established for conducting near real-time and long-term monitoring. The distances between the base and the rover antennas are within two kilometers. The network was continuously operating for eight months from April to December 2018. The method is comprised of three components: (1) Monitoring the stability of the base station using precise point positioning (PPP) method, a stable regional reference frame, and a seasonal ground deformation model; (2) monitoring the relative positions of rover stations using the carrier-phase double-difference (DD) positioning method in near real-time; and (3) detecting abrupt and gradual displacements at both base and rover stations using an automated change point detection algorithm. The method is able to detect abrupt positional-changes as minor as five millimeters in near real-time and gradual positional-changes at a couple of millimeters per day within a week. The method has the flexibility of concurrent processing different GPS and BeiDou data sessions (e.g., every 15 minutes, 30 minutes, one hour, one day) for diffident monitoring purposes. This study indicates that BeiDou observations can also achieve few-millimeter-accuracy for measuring displacements. Parallel processing GPS and BeiDou observations can improve the reliability of near real-time structural deformation monitoring and minimize false alerts. The method introduced in this article can be applied to other urban areas for near real-time and long-term structural health monitoring. [ABSTRACT FROM AUTHOR]

Air-borne particulate matter, PM2.5 (PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM2.5 distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM2.5 and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 µg/m3 and the highest coefficient of determination regression score function (R2) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 µg/m3 compared to SARIMA's 17.41 µg/m3. Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM2.5 in time, and it can also eliminate better the spatial predicted errors compared to SARIMA. [ABSTRACT FROM AUTHOR]

Samples of atmospheric depositions from five types of functional areas in Shijiazhuang, Hebei Province, China, were collected, and the concentrations of six toxic heavy metals (Cd, Cr, Cu, Pb, Ni, and Zn) were measured. Geographic information system, Pb isotope assessment, multivariate statistical analysis (principal component analysis, PCA), the geoaccumulation index (Igeo), potential ecological risk index (PERI), and a health risk assessment model were used to study the degree of pollution, identify sources of pollution, and assess the health risks to children and adults via three pathways (hand–mouth intake, skin contact, and respiration). The results show that the high traffic volume and exhaust gas emissions have led to high concentrations of heavy metals. The Igeo and PERI values of Cd (0.38–2.0 and 108–4531, respectively), indicating the present high pollution level and potential risk, respectively, varied the most. Pb isotope and PCA showed that Pb, Zn, and Cd from atmospheric deposition come from power plants and traffic—Cu is related to traffic, and Ni and Cr come mainly from soil particles (natural source). The health risk assessment showed that heavy metals in atmospheric depositions are at a safe level in the study area. [ABSTRACT FROM AUTHOR]