Semi-variance Function Research Articles

Spatial Distribution, Source Apportionment, and Pollution Assessment of Toxic Metals Around Agricultural Soils Based on APCS-MLR Receptor Modelling: A Case Study of the Northern Slope of Tianshan Mountains

To investigate the contamination status and analyze the sources of soil toxic metal contamination on the northern slopes of the East Tianshan mountain industrial belt in Xinjiang, northwest China, this study measured the contents of six common toxic metals such as Zn, Cu, Cr, Pb, Hg and As in 82 surface soil (0–20 cm), and using the ground accumulation index, pollution load index, and improved weighted index assessed the contamination characteristics of the soil and using a semi-variance function and APCS-MLR model the identified the potential sources of contamination. The results indicate that the average concentrations of Pb, Hg, and As are significantly higher than the background values in Xinjiang. The average ranking of toxic metal content is as follows: Zn > Cr > Pb > Cu > Hg > as. A single-factor pollution index analysis shows that As and Pb pollution are severe, while Hg and Cu pollution are moderate. The improved weighted index shows that moderate lead pollution accounts for 6.1% and severe lead pollution accounts for 54.88%; 98.88% of arsenic is severely contaminated. The APCS-MLR model identified three main sources of heavy metals: Cu and Cr as industrial production sources, Pb and Zn as transportation and agricultural activity sources, and As, Cr, Cu, Hg, and Zn as natural and mixed pollution sources. This study provides a solid scientific basis for the prevention and control of toxic metal pollution in agricultural soils, thus ensuring food security and sustainable development in the region.

Spatiotemporal Correlation Analysis of Landscape Pattern and Habitat Quality in and Around China’s Tropical Rainforest National Park

With the establishment of the national park system in China, balancing ecological conservation within national parks and development in surrounding areas has become a critical issue. Despite its importance, studies on the scale effects of landscape fragmentation and its relationship with habitat quality (HQ) in these regions remain scarce. Therefore, this study focuses on the Hainan Tropical Rainforest National Park (HTRNP) in China and the surrounding nine cities and counties. The optimal scale for analyzing the spatiotemporal characteristics of landscape patterns, we assessed the best grain size and extent of landscape pattern indices using the regional information loss assessment model, area loss index, and semivariance function analysis. Based on this, the Fragstats and InVEST models analyzed the spatiotemporal evolution of landscape patterns and HQ from 1980 to 2020. Additionally, bivariate spatial autocorrelation theory was applied to examine the spatial correlation between the two. The results show that (1) the landscape indices in the study area vary with grain size and extent. The optimal grain size is 300 m, and the optimal extent is 2 km × 2 km. (2) Over the past 40 years, the landscape pattern of HTRNP has remained stable. In contrast, the surrounding areas exhibit notable spatial differentiation, with landscapes in coastal town centers becoming increasingly fragmented. (3) Over the past 40 years, the overall HQ in the study area has been relatively high, showing a spatial distribution pattern that decreases gradually from the center of HTRNP outward to rural areas, towns, and coastal regions. HTRNP has maintained a high and stable HQ, while the surrounding areas exhibited a decreasing trend in HQ, although the decline was relatively modest. (4) The spatial distribution of HQ aligns closely with landscape patterns, indicating a significant spatial correlation. The landscape connectivity of HTRNP remains high, with consistently superior HQ. In contrast, coastal town centers are characterized by highly fragmented landscapes and poor HQ. This study reveals the spatiotemporal dynamics of landscape patterns and HQ, along with their spatial associations. These findings provide a scientific basis for ecological conservation in national parks and the planning optimization of surrounding areas.

Detection of uniformity and spatial correlation of subgrade compaction quality based on intelligent compaction technology

Intelligent compaction uniformity and spatial correlation detection of subgrade is an important part of intelligent compaction technology. In this paper, a subgrade compaction uniformity detection method is established based on the univariate statistical normal distribution 3σ criterion, and further a subgrade compaction quality spatial correlation detection method is established by the multivariate statistical spatial semi-variance function, and finally the established subgrade compaction uniformity and spatial correlation methods are verified by field tests. The subgrade compaction uniformity and spatial correlation are detected based on field tests. The results obtained in this paper show that the 3σ criterion based on normal distribution can be used for the detection of intelligent compaction uniformity of subgrade, and the field test results are verified by this method, and all the intelligent compaction uniformity of subgrade meets the requirements; considering the construction efficiency and the influence between rollers, it is recommended that the rolling speed should be in the interval range of 2–3 m/s, and the construction spacing of rollers is maintained at 10 m and above.

Spatial Differentiation Characteristics and Influencing Factors of Surface Soil Cd in Karst Plateau Area of Guiyang City

In order to explore the spatial differentiation characteristics and variation law of soil Cd content in a high geological background area, 14 421 topsoil samples were collected from topsoil in the karst area of Guiyang City. Global Moran's I index, cold hot spot analysis, semi-variance function, and Kriging interpolation were used to reveal the spatial structure and distribution law of soil Cd content. The influence of environmental factors on soil Cd content and its main controlling factors were analyzed through analysis of variance and geographic detector. The results showed that： ① The Cd content of karst surface soil in Guiyang varied from 0.03 to 1.36 mg·kg-1, with an average of 0.440 mg·kg-1, which was 1.77 times and 5.95 times the Guizhou soil Cd background value and Chinese soil Cd background value, respectively. The over-standard rate of soil Cd was 30%, which was 4.29 times that of 7% of soil Cd in China. ② There was a significant spatial positive correlation of soil Cd content, showing an aggregation trend in the global space, whereas in the local region, the northeast and southwest were hot spots, and the north was a cold spot. The nugget coefficient of soil Cd content was 10.37%, indicating that soil Cd was mainly affected by structural factors. ③ In terms of spatial distribution, soil Cd showed different accumulation trends. In some massive soils, such as Xifeng County, Xiuwen County, Qingzhen City, Huaxi District, and Nanming District, the soil ω（Cd）was less than 0.3 mg·kg-1. The soil ω（Cd）was between 0.3 and 0.6 mg·kg-1,and soil Cd in Baiyun District, Wudang District, Guanshan Lake area, and Yunyan area as a whole lied within this range. The soil ω（Cd）between 0.6 and 0.9 mg·kg-1 was concentrated in the southwest of Qingzhen City, the south of Huaxi District, and the north of Kaiyang County, whereas soil ω（Cd） between 0.9 and 1.2 mg·kg-1 was mainly concentrated in the southwest of Qingzhen City. The extreme value of soil Cd content （ > 1.2 mg·kg-1） was mostly distributed in Kaiyang County, Xiuwen County, Qingzhen City, and Huaxi District. ④ The results of analysis of variance and geo-detector showed that different environmental factors had significant effects on the spatial differentiation of soil Cd, but their explanatory power on soil Cd content varied： stratum （0.176 5） > soil type （0.026 0） > organic matter （0.025 1） > altitude （0.010 5） > parent rock （0.007 3） > land use （0.006 4） > pH （0.001 3）, and the interaction between stratum and arbitrary environmental factors was the greatest. Therefore, stratum was the main factor affecting the spatial differentiation of soil Cd content.

Analysis of landscape pattern vulnerability in Dasi river basin at the optimal scale

Since the reform and opening up in 1978, the Dasi River Basin within Jinan’s startup area from replacing old growth drivers with new ones (startup area) has experienced rapid urbanization and industrialization, and the landscape pattern has changed significantly, resulting in a series of eco-environmental problems. In order to more accurately identify the vulnerable areas of landscape pattern, understand their cause mechanism and changing laws, and provide a theoretical basis for the implementation of sustainable landscape pattern planning and management in the region. Four Landsat images of 2002, 2009, 2015 and 2020 were taken as data sources, and the optimal granularity of landscape pattern analysis was determined from the perspective of landscape level and class level by using the coefficient of variation method, granularity effect curve and information loss model, and the optimal amplitude was determined by using the grid method and semi-variance function. Then, the landscape vulnerability assessment model was constructed based on the optimal scale, and its spatiotemporal evolution characteristics and spatial autocorrelation were analyzed. The result showed that: (1) The optimal granularity of landscape pattern analysis in this study area was 80 m, and the optimal amplitude was 350 × 350 m. (2) During 2002–2020, the overall vulnerability of landscape pattern in the southern part of the study area showed an increasing trend, while that in the middle and northern parts showed a decreasing trend. (3) The mean values of the vulnerability index of the overall landscape pattern in 2002, 2009, 2015 and 2020 were 0.1479, 0.1483, 0.1562 and 0.1625, respectively, showing an increasing trend year by year. In terms of land use, during 2002–2020, the average vulnerability indices of forestland and built up land increased by 23.18% and 21.43%, respectively, followed by water body and bare land, increased by 12.18% and 9.52%, respectively, while the changes of cropland and grassland were relatively small, increasing by 5.36% and 5.65%, respectively. (4) During 2002–2020, the landscape pattern vulnerability showed a significant spatial positive correlation in terms of spatial distribution. The Low-Low areas were generally transferred from the southeastern and midwestern to the middle and northern, and the High–High areas were mainly transferred from the middle to the southern. Overall, the degree of the spatial agglomeration of the landscape pattern vulnerability showed an increasing trend.

Analysis of landscape pattern and ecological risk change characteristics in Bosten Lake basin based on optimal scale

Amidst swift socioeconomic progress, urbanization is intensifying, necessitating robust methodologies for environmental quality monitoring. Landscape Ecological Risk (LER) has emerged as a pivotal tool in this context. Extensive research has focused on the land utilization rates within watersheds, scrutinizing them through the lens of land use/land cover (LULC) dynamics. This study delves into the LULC attributes of the Bosten Lake Basin (BLB) across three temporal snapshots: 2000, 2010, and 2020. Grasslands and bare ground made up the vast majority area of the BLB. Selected twelve landscape pattern indices, analyzed their response curves with respect to grain size, determined the optimal grain size to be 150 m, and analysed its optimum amplitude of 10 km × 10 km on the basis of the semi-variance function, thus determined the optimal scale for analyzing the landscape pattern. Exploring scale effect facilitates a nuanced examination of the landscape patterns and land use efficacy within BLB. From 2000 to 2020, the landscape pattern showed less fragmented, less aggregated, and more spatially heterogeneous. The landscape ecological risk index model has been constructed for the investigation of the LER change characteristics of the BLB. Lowest-risk zones and lower-risk zones of LER predominate in BLB. In 2000, 2010 and 2020 the combined lower and lowest risk zones represent 62.02 %, 59.37 % and 58.44 % of the catchment area respectively. Finally, analyzed the spatial autocorrelation variation characteristics of LER. The global Moran' I index of BLB for the three periods from 2000 to 2020 exceeded 0.7. This study establishes theoretical and practical foundation for future multi-scale landscape ecological risk studies in eco-fragile regions.

DEM-based topographic change detection considering the spatial distribution of errors

ABSTRACT Digital Elevation Model (DEM) errors tend to be spatially correlated, inevitably affecting DEM-based topographic change detection. Traditional topographic change detection methods often ignore the spatial distribution of the DEM error. This paper aims to develop a workflow that considers the spatial autocorrelation of the error in topographic change detection. Firstly, the DEM of Difference (DoD) is obtained from two-period DEMs, and the Monte Carlo method is employed to evaluate the Spatially Distributed Errors (SDE) in DEMs. Secondly, DoD errors are calculated by propagation based on spatially distributed DEM errors. At the same time, its spatial distribution is quantified using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial distribution analysis and significance detection. The results in two small catchments indicate that DEM errors are spatially correlated, increasing the volume calculation errors. However, using Standard Deviation of Errors (SDE) instead of Root Mean Square Error (RMSE) can effectively reduce the sensitivity of the detection results in the significance threshold. When the significance threshold increases from 68% to 95%, the observations loss using the spatially distributed error is 4.67% −6.92% lower than that using the RMSE. The level of detection has little impact on the net topographic change and significantly influences gross erosion and deposition. In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. The proposed method can be effectively utilized in various applications like surface deformation monitoring, erosion monitoring, and sediment transport assessment.

Source analysis and contamination evaluation of potentially toxic elements in soil in typical high geological background areas at the county scale in northeastern Yunnan, China

The pollution risk of potentially toxic elements (PTEs) in soil is a common concern in the fields of environmental science and agriculture. The spatial distribution characteristics, pollution risk assessment and genetic sources of PTEs in soil in high geological background areas are crucial to the safe use of land. Based on the data for five PTEs in 921 surface soil samples from typical high geological background areas in SW China, we discuss their spatial distribution characteristics using geostatistics, the semivariance function and its model. We carry out pollution risk assessment using the influence index of comprehensive quality (IICQ) and analyse their genetic sources using the UNMIX receptor model. The spatial distribution of element content has a high spatial coupling with the stratigraphic spread, which is affected by external random factors, resulting in a weakened autocorrelation. The IICQ indicates that soil is dominated by mild and moderate pollution, accounting for 42.24 and 44.95%, respectively, with cadmium contributing up to 63.00% to the IICQ. Chromium and cadmium are controlled by natural sources, accounting for 83.98 and 69.39%, respectively. Arsenic and mercury have anthropogenic sources, accounting for 68.7 and 44.0%, respectively. Lead is controlled by both lead–zinc mining and geological background, accounting for 47.7 and 36.4%, respectively. The UNMIX model demonstrates that the spatial variability of PTEs is influenced by anthropogenic factors, such as coal combustion emissions, which account for 30.1% of the pollution sources. Soil parent material sources contribute 49.4%, followed by lead–zinc mining and smelting activities at 20.5%.

Spatial planning constraints will mitigate the fragmentation trajectory of natural and semi-natural landscapes: a case of Lushan City, China

ContextThe biodiversity faces an underlying threat from landscape fragmentation resulting from rapid urbanization. Examining the future trajectory of landscape fragmentation is imperative to understanding the impact of current spatial planning constraints on natural and semi-natural habitat preservation, ecosystem services, and sustainability.MethodsWe employed a Patch-generating Land Use Simulation (PLUS) model to simulate and predict the land use and landscape pattern changes in Lushan City under two distinct scenarios: “Planning Constraints (PC)” and “Natural Development (ND)”. We then identified an appropriate landscape fragmentation index (LFI) that effectively captured the fragmentation essence. To determine the optimal scale, we adopted an experimental approach using both the moving window (MW) method and the semi-variance function. By constructing a LFI spatiotemporal sequence and conducting trend analysis, we identified the potential fragmentation areas (PFA) with significant fragmentation tendencies.ResultsThe spatial planning constraints will (1) prevent the encroachment of construction land into 2.14 km2 of cropland, 0.21 km2 of forest, and 0.13 km2 of grassland; (2) shift the highly fragmented area from the northeastern portion of Lushan to the planned area defined by the development boundary; (3) mitigate and decelerate the trend of landscape fragmentation in natural and semi-natural landscapes, decrease PFA by 7.74 km2 and preserve 15.61 km2 of natural landscapes. (4) still leave 29.42% of forest and 22.82% of grassland at risk of fragmentation.ConclusionsSpatial planning constraints will effectively control the potential fragmentation in natural and semi-natural landscapes by changing the spatial distribution of LFI and PFA. This control mechanism will greatly exclude the anthropogenic impact and ensure the conservation of habitats. The habitats remaining within PFA should be focused in future eco-management optimization.

The Distribution Characteristics and Potential Risk Assessment of Lead in the Soil of Tieguanyin Tea Plantations in Anxi County, China.

Assessing the distribution and risks associated with the soil lead content in the Tieguanyin tea plantations of Anxi County is critical, given the county's significance as the primary Tieguanyin tea production area in Fujian Province. This study examined the distribution characteristics of soil lead in Anxi County's tea plantations according to the Kriging spatial interpolation of the parameters of the semivariance function of the exponential model. Moreover, the sources of lead content were analyzed, considering geological backgrounds and anthropogenic influences. Ecological risks and the issuance of early warnings were also assessed. The soil lead content in the rocks of the Tieguanyin tea plantations in Anxi County followed the order: andesite > dacite > rhyolite > granite. The soil lead content gradually decreased from the center toward the east and west, forming four distinct north-south parallel zones. High-lead-content areas were identified at the border of Jiandou, Bailai, and Hushang; in the central part of Lutian; and in the southern part of Huqiu. The high levels of soil lead in the tea plantations possibly originated from industrial and mining activities, automobile exhaust, and agricultural activities. The distribution of single-factor pollution indices and potential risk evaluation based on the Soil Environmental Quality Standard, Environmental Technical Conditions for Tea Production Area, and Environmental Technical Conditions for Organic Tea Production Area indicated that the soil in Tieguanyin tea plantations in Anxi County was clean and safe for tea cultivation.

Spatial Distribution and Pollution Assessment of Heavy Metals in Soils in the Jiefangzha Irrigation Area of the Hetao Irrigation District, China

To explore the characteristics of heavy metal pollution in farmland soils in the Jiefangzha Irrigation Area of the Hetao Irrigation District, Inner Mongolia, 60 samples of the surface soil (0–20 cm) were collected to detect and analyze the content of typical heavy metal elements, Cu, As, and Pb, in the soil. The methods utilized included the single factor index method, the Nemerow index method, the geological accumulation index method, and the potential ecological hazard index method to evaluate their pollution and ecological risks, and these were combined with geostatistical methods using GIS (Geographic Information System) technology to quantitatively analyze their spatial distribution characteristics. The results showed that the average contents of Cu, As, and Pb did not exceed the background values in the Hetao Irrigation Area, and the contents showed a moderate variation, with the order of variation as As > Cu > Pb. The multivariate statistical analysis results showed that the three elements, Cu, As, and Pb, had similar sources, i.e., primarily the use of agrochemicals, such as fertilizers and pesticides. The semi-variance function model fitting results showed that Pb followed a spherical model, and Cu and As followed Gaussian models. Kriging interpolation showed that the contents of As, Pb, and Cu, in general, showed a trend that was higher in the northwest and lower in the southeast, with a change of gradient increasing from the southeast to the northwest. Human activities were the primary factors causing the distribution difference.

Analysis of Spatial and Temporal Variability and Coupling Relationship of Soil Water and Salt in Cultivated and Wasteland at Branch Canal Scale in the Hetao Irrigation District

The Hetao Irrigation District is a typical salinized irrigation district in China, and soil salinization restricts agricultural development. To explore the spatial and temporal variability of soil water and salt and the coupling relationship in the Hetao Irrigation District, a field experiment was carried out at the scale of the Yichang Irrigation District branch canal in the downstream of the Hetao Irrigation District. Fifty-three soil sampling points were established to analyze the spatial and temporal variability of soil water content and total salt content and the coupling relationship using geostatistics and the coupling degree model. The results showed that soil water content in the study area belonged to medium variability and weak variability, and soil total salt content belonged to strong variability and medium variability. The theoretical models of soil water content and total salt content semi-variance function in the study area following the Gaussian model, with the block-base ratio less than 25%, with strong spatial autocorrelation, and the spatial correlation gradually increased with the increase of soil depth. The total salt content of the soil in the study area was interpolated with higher accuracy using radial basis functions as compared to ordinary kriging interpolation. In terms of temporal changes in salinity, the average salt accumulation rate of the 0–100 cm soil layer in the study area was 20.17% when salinity increased from May to June; the average desalination rate was 16.37% when salinity decreased from June to August. The main factors affecting soil salinity in cultivated land during the growing period were irrigation, precipitation, and planting crops, and the main factors affecting soil salinity in wasteland were precipitation and topography. The average coupling degree of soil water and salt in wasteland in the study area was lower than that of cultivated land, ranging from 65.15% to 86.59% of that of cultivated land. The level of coordination is marginal coordination for cultivated land and marginal disorder for wasteland. The study provides a theoretical basis for the prevention and control of soil salinization in arid areas.

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

Accurately estimating forest biomass based on spaceborne lidar on a county scale is challenging due to the incomplete coverage of spaceborne lidar data. Therefore, this research aims to interpolate GEDI spots and explore the feasibility of approaches to improving Quercus forest biomass estimation accuracy in the alpine mountains of Yunnan Province, China. This paper uses GEDI data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Based on the pre-processing of light spots. A total of 38 parameters were extracted from the canopy and vertical profiles of 1307 light spots in the study area, and the polygon data of the whole study area were obtained from the light spot data through Kriging interpolation. Multiple linear regression, support vector regression, and random forest were used to establish biomass models. The results showed that the optimal model is selected using the semi-variance function for the Kriging interpolation of each parameter of GEDI spot, the optimal model of modis_nonvegetated is a linear model, and the optimal model for rv, sensitivity, and modis_treecover is the exponential model. Analysis of the correlation between 39 parameters extracted from GEDI L2B and three topographic factors with oak biomass showed that sensitivity had a highly significant positive correlation (p < 0.01) with Quercus biomass, followed by a significant negative correlation (p < 0.05) with aspect and modis_nonvegation. After variable selection, the estimation model of Quercus biomass established using random forest had R2 = 0.91, RMSE = 19.76 t/hm2, and the estimation accuracy was better than that of multiple linear regression and support vector regression. The estimated total biomass of Quercus in the study area was mainly distributed between 26.48 and 257.63 t/hm2, with an average value of 114.33 t/hm2 and a total biomass of about 1.26 × 107 t/hm2. This study obtained spatial consecutive information using Kriging interpolation. It provided a new research direction for estimating other forest structural parameters using GEDI data.

Distribution and attribute analysis of soil selenium in Hebei Province, China

From 2004 to 2018, c. 73% of the land area in Hebei Province was surveyed by a 1:250 000 land quality geochemical survey of the National Geochemistry Survey of Land Quality. Based on the topsoil (0–20 cm) datasets (50 724 analysis samples) and attribute analysis of the key area with a semivariance function model, geographic detector model and random forest model, the content and spatial distribution of soil selenium (Se) in Hebei Province and the influencing factors of the distribution of Se in the Shijiazhuang–Xingtai–Handan area were obtained. The soil Se content in Hebei Province is low, ranging from 0.02 to 3.23 mg kg –1 , with an average of 0.20 mg kg –1 . The spatial distribution of soil Se was heterogeneous, and the marginal Se (0.125–0.175 mg kg –1 ) area and the sufficient Se (0.175–0.40 mg kg –1 ) area were widely distributed, but the acreage of the Se-rich (0.40–3.0 mg kg –1 ) area was very limited, accounting for only 2.43% of the total survey area. However, the Shijiazhuang–Xingtai–Handan area was a significant Se enrichment area. The spatial distribution of Se in this area was restricted by natural and socioeconomic factors. Soil organic matter was the most influential factor and showed good coordination with Se. Coal burning superimposed on natural factors promotes the spatial differentiation of soil Se to a certain extent.

Evaluation of InSAR Tropospheric Delay Correction Methods in a Low-Latitude Alpine Canyon Region

Tropospheric delay error must be reduced during interferometric synthetic aperture radar (InSAR) measurement. Depending on different geographical environments, an appropriate correction method should be selected to improve the accuracy of InSAR deformation monitoring. In this study, surface deformation monitoring was conducted in a high mountain gorge region in Yunnan Province, China, using Sentinel-1A images of ascending and descending tracks. The tropospheric delay in the InSAR interferogram was corrected using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 meteorological reanalysis data (ERA5) methods. The correction effect was evaluated by combining phase standard deviation, semi-variance function, elevation correlation, and global navigation satellite system (GNSS) deformation monitoring results. The mean value of the phase standard deviation (Aver) of the linear correction interferogram and the threshold value (sill) of the semi-variogram were reduced by –20.98% and –41%, respectively, while the accuracy of the InSAR deformation points near the GNSS site was increased by 58%. The results showed that the three methods reduced the tropospheric delay error of InSAR deformation monitoring by different degrees in low-latitude mountains and valleys. Linear correction was the best at alleviating the tropospheric delay, followed by GACOS, while ERA5 had poor correction stability.

On the uncertainty of estimating photovoltaic soiling using nearby soiling data

The accumulation of dust on the surface of photovoltaic modules can reduce their performance and affect the cost competitiveness of this technology. This phenomenon is known as soiling and can be mitigated through appropriate corrective and/or preventive actions. In order to maximize its effectiveness, it is important to plan the soiling mitigation strategy even before the PV system is operational. This is typically done through a nearest neighbor approach, by estimating soiling using data from the nearest operational photovoltaic system. This work focuses on understanding the uncertainty related to this practice. For this purpose, the semi-variance function is used to study the dissimilarity between the soiling losses of two locations in California depending on their distance. The results show that, when the soiling loss at a nearby system is used to estimate soiling of a site, the uncertainty can be approximated to increase linearly at a rate of 0.08–0.10%/km up to 60 or 80 km. After this distance, the use of a nearest neighbor approach is no longer justified, as it produces an uncertainty as big as the average soiling loss of the sites in the dataset used in this study. In some conditions, uncertainties > 0% are found also for sites located within 25 km, meaning that even close-by systems might soil differently.

Study on the Compaction Performance and Its Key Influencing Factors of Asphalt Mixture Based on Intelligent Sensing Techniques

Real‐time and accurate compaction quality monitoring is one of the key steps in the pavement construction. In this study, the effect of roller passes and temperature on the compactness of asphalt mixtures and its uniformity is discussed using the semivariance function. Further, the indoor asphalt mixture gyratory compaction test was carried out based on the embedded SmartRock sensor, with the aim of monitoring the changes in rotation and contact forces between aggregates. In addition, the effect of temperature on the dynamic response of SmartRock is discussed, and the compaction mechanism of asphalt mixtures is investigated from a microscopic scale. It is found that compaction degree and uniformity are not always positively correlated with the number of roller passes and that excessive compaction leads to a decrease in compaction effect; meanwhile, the variation in the compactness of asphalt mixtures can be characterized by the dynamic response of SmartRock, which providing a feasible method for real‐time monitoring of the compaction process and optimization of the construction plan.

An improved altimeter-derived gravity anomaly from shipborne gravity based on the mean sea surface height constraint factors method

Coastal marine gravity modeling faces challenges due to the degradation of the quality and poor coverage of altimeter data in coastal regions. The effective fusion of shipborne gravity data and altimeter-derived marine gravity data can make shipborne gravity data more useful for the accurate estimation of altimeter-derived coastal marine gravity. A mean sea surface height constraint factor (MSSHCF) method based on the ordinary kriging method and the remove-restore technique is proposed to fuse altimeter-derived gravity model with shipborne gravity data. In this method, all data are standardized during the interpolation process to reduce the error and mean sea surface as a vertical variable is added to the semi-variance function in ordinary kriging to obtain the residual shipborne gravity as corrected data source. The coastal marine gravity models V2.1 and V3.1 which fused altimeter-derived gravity data with shipborne gravity data and V1.1 without shipborne gravity data at a spatial resolution of 1′×1′ can be obtained. Validation experiments show that the accuracy of the gravity model V3.1 obtained by the MSSHCF method more closely agrees with the validated gravity model DTU17 and SS V31 than the model V2.1 obtained by the ordinary kriging interpolation method and the V1.1 model. Our results were validated against shipborne gravity data; the accuracy of model V3.1 was 4.95 % higher than the model V1.1 in South China Sea area A and 2.48 % higher in South China Sea area B. Meanwhile, the accuracy of model V3.1 was 2.07 % higher than model V2.1 in South China Sea area A and 2.42 % higher in South China Sea area B. The effects of distance from the coast and sea depth on the marine gravity model were also evaluated. The results show that the gravity model V3.1 has higher accuracy with the change in ocean distance and depth than the V2.1 and V1.1 gravity models. Thus, our study shows that the MSSHCF method effectively refines coastal altimeter-derived gravity using shipborne gravity data.

An Improved Coastal Marine Gravity Field Based on the Mean Sea Surface Height Constraint Factor Method

Construction of a high spatial resolution and high precision marine gravity field in coastal areas is constrained by the low quality and sparse coverage of altimetry data, except for limited shipborne and airborne gravity surveys. To address this problem, a mean sea surface height constraint factor (MSSHCF) method based on the ordinary kriging method and the remove-restore technique is proposed from the perspective of interpolation. In this method, the data is standardized during the interpolation process to reduce the error and mean sea surface as variables related to the marine gravity field are added to the semi-variance function in ordinary kriging to obtain a marine gravity field with a spatial resolution of 1′ × 1′. Validation experiments show that the MSSHCF method more closely agrees with the referenced SS V28, DTU17 global marine gravity models than the ordinary kriging method. Our results were further validated against shipborne data; the accuracy of the MSSHCF method is 0.13 and 0.33 mGal higher than that of the ordinary kriging method in two experimental areas. The effects of ocean depth and offshore distance on the results were also assessed. These results show that the proposed method is more accurate than the ordinary kriging method, when the distance and depth varied. Therefore, our study demonstrates that the MSSHCF method is an innovative and feasible tool for extracting gravity fields along coastal, beach, and island areas.

Spatial Pattern Evolution Characteristics and Influencing Factors in County Economic Resilience in China

In the face of shocks, a region’s economic resilience decides whether it can quickly recover or slip into long-term economic stagnation. This study took 2801 counties in China as the research object and distinguished them into long-term and short-term economic resilience by taking 2007–2020 as the research time period, and used spatial autocorrelation, the semi-variance function, and the geodetector method to analyze the spatial evolution pattern and driving mechanism of economic resilience of China’s counties in different time periods. The research found that: (1) From a long-term perspective, the economic resilience of China’s counties was dominated by the moderate level of resilience, and although its characteristics varied slightly over time, the overall performance showed that the level of resilience was increasing. Over time, the number of counties with very high levels of resilience has been increasing, and the number of counties with very low levels has been gradually decreasing. (2) In terms of spatial layout, China’s county economic resilience exhibited spatial autocorrelation, with similar areas clustered and distributed spatially, with high-high concentration (H-H) and hot spot (99% confidence) areas distributed in the eastern coast and its hinterland, and low-low concentration (L-L) and cold spot (99% confidence) areas distributed in Inner Mongolia and the northeast. The evolution of its spatial pattern was influenced by both stochastic and structural factors, and the spatial divergence was mainly reflected in the northeast–southwest direction, while the northwest–southeast direction was more balanced. (3) Long-term economic resilience and short-term economic resilience had different influencing factors. The industrial structure diversification index, which characterized economic factors, could significantly improve the long-term economic resilience of cities, while the influencing factors of short-term economic resilience differed from period to period.