Nonlinear Enhancement Research Articles

Global spatial and temporal dynamics of the green water coefficient and analysis of factor from 1992 to 2020

In recent years, the impacts of climate change have variably affected the global ecological environment, exacerbating water scarcity and related issues. Meantime, as part of soil evapotranspiration and plant transpiration in water resources, green water is of great significance in stabilizing the global water cycle and promoting the sustainable development of agriculture. Therefore, the green water coefficient in this study calculates the conversion rate of precipitation to green water, which is important for the balance between blue and green water and the stability of the ecological environment. Based on trend analysis methods and Geodetector, this study analyses the spatial and temporal characteristics and driving mechanisms of the green water coefficient on the globe and on each land-use type from 1992 to 2020. The findings revealed that: (1) The global green water coefficient is clearly spatially differentiated, showing certain latitudinal distribution differences, with the lowest near the equator. The areas with higher green water coefficients are mainly in northern and southern Africa, central Asia, Oceania and northern and western North America, while the areas with lower green water coefficients are concentrated in northern South America and Southeast Asia. (2) The global average value of green water coefficient decreases before increasing. Except for water, the green water coefficient of other land use types (crop land, forest land, grassland, urban land, unused land) changes significantly and there are fewer abrupt change points. Areas where the green water coefficient is too low and changes drastically are not suitable for rainfed agriculture. (3) Different influential factors act on the green water coefficients of different land use types, and there is a two-factor enhancement and non-linear enhancement relationship between them, with a combination of factors affecting the green water coefficient. By studying the characteristics of the green water coefficient globally and across different land use types, we quantified the influence of climate factors such as precipitation, temperature, wind speed, evapotranspiration, radiation, etc., on the green water coefficient, thereby providing some information for future global rain-fed agriculture and water resources management.

Augmented and Virtual Reality Based Segmentation Algorithm for Human Pose Detection in Wearable Cameras

Pose graph optimisation is a crucial method that helps reduce cumulative errors while estimating visual trajectories for wearable cameras. However, when the posture graph's size increases with each additional camera movement, the optimization's efficiency diminishes. In terms of ongoing sensitive applications, such as extended reality and computer-generated reality, direction assessment is a major test. This research proposes an incremental pose graph segmentation technique that accounts for camera orientation variations as a solution to this challenge. The computation only improves the cameras that have seen large direction changes by breaking the posture chart during these instances. As a result, pose graph optimisation is essentially slowed down and optimised more quickly. For every camera that hasn't been optimised using a pose graph, the algorithm employs the wearable cameras at the start and end of each camera's trajectory segment. The final camera in attendance is then determined by weighted average the various postures evaluated with these wearable cameras; this eliminates the need for lengthy nonlinear enhancement computations, reduces disturbance, and achieves excellent accuracy. Experiments on the EuRoC, TUM, and KITTI datasets demonstrate that pose graph optimisation scope is reduced while maintaining camera trajectories accuracy.

Identification of Key Determinants Influencing Spatiotemporal Heterogeneity of Urban Resilience

The identification of spatiotemporal heterogeneity, its key determinants, and the interaction effects between the driving factors of urban resilience (UR) within and between subregions is fundamental for understanding its underlying mechanisms. A resilience evaluation model was applied to analyze the temporal and spatial differences in UR in Hunan Province, China. A hot spot analysis identified clusters of cold and hot spots. The contributions of spatiotemporal differences in UR within and between subregions were assessed using the Theil index. A geodetector analysis determined the factors influencing UR and their interactions. There was an increasing trend in UR from 0.2692 in 2014 to 0.3422 in 2022. The number of cities with a high resilience gradually increased from 2014 to 2022, while there was a decreasing gradient in UR from northeast to southwest across the province. High-resilience cities had positive spillover effects on the surrounding area. Hot spots were predominantly located in the northeast, while cold spots were concentrated in the southwest. The barycenter of UR shifted from northeast to southwest by 2018, before moving southeast by 2022. The Theil index values declined over time both within and between subregions. Per capita GDP, the average wages of on-the-job employees, per capita social consumption, and doctor density were found to enhance UR. Two-factor interactions had a greater influence on the spatiotemporal heterogeneity of UR than single factors. Two-factor and nonlinear enhancements were identified as the primary mode of influence.

Climate change and vegetation greening jointly drive the spatial pattern of net radiation variability in northern China

ABSTRACT The spatial and temporal variations of net surface radiation (Rn) are critical for comprehending ecological environments. Nonetheless, the intricate interplay among Rn dynamics, vegetation growth, climate, and natural factors remains inadequately elucidated. In this study, we estimated net surface radiation based on Landsat data and ERA5 meteorological data in the Google Earth Engine (GEE) platform, which closely matched the observable spatial distribution (R 2 = 0.96), with an average growth rate of 0.15 MJ m−2 mth−1. Trend analyses and spatial autocorrelation were used to explore the spatial and temporal changes in net radiation from 2000 to 2020, the global Moran's index for net radiation was found to exceed 0.76, with fluctuating increases, showing a highly positive spatial distribution of Rn. Local Moran's I predominantly fell into two categories: ‘High-High’ and ‘Low-Low’, with the first increasing in range and the latter decreasing. Combining GeoDetector and PLS-SEM analyses, temperature and vegetation emerge as predominant drivers of net surface radiation variation within the study area, each contributing more than 17% to Rn change. Furthermore, interactions between any two factors typically exhibits nonlinear enhancement. PLS-SEM underscores the influence of vegetation and climate on Rn, with other factors indirectly affecting net radiation changes by influencing vegetation growth.

Retraction Note: A 9-bit pseudo-noise-based calibrated successive approximation ADC with differential/integral nonlinearity enhancement

Retraction Note: A 9-bit pseudo-noise-based calibrated successive approximation ADC with differential/integral nonlinearity enhancement

Single-view 3D reconstruction via dual attention

Constructing global context information and local fine-grained information simultaneously is extremely important for single-view 3D reconstruction. In this study, we propose a network that uses spatial dimension attention and channel dimension attention for single-view 3D reconstruction, named R3Davit. Specifically, R3Davit consists of an encoder and a decoder, where the encoder comes from the Davit backbone network. Different from the previous transformer backbone network, Davit focuses on spatial and channel dimensions, fully constructing global context information and local fine-grained information while maintaining linear complexity. To effectively learn features from dual attention and maintain the overall inference speed of the network, we do not use a self-attention layer in the decoder but design a decoder with a nonlinear reinforcement block, a selective state space model block, and an up-sampling Residual Block. The nonlinear enhancement block is used to enhance the nonlinear learning ability of the network. The Selective State Space Model Block replaces the role of the self-attention layer and maintains linear complexity. The up-sampling Residual Block converts voxel features into a voxel model while retaining the voxels of this layer. Features are used in the up-sampling block of the next layer. Experiments on the synthetic dataset ShapeNet and ShapeNetChairRFC with random background show that our method outperforms recent state of the art (SOTA) methods, we lead by 1% and 2% in IOU and F1 scores, respectively. Simultaneously, experiments on the real-world dataset Pix3d fully prove the robustness of our method. The code will be available at https://github.com/epicgzs1112/R3Davit.

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Spatial-temporal Change and Driving Force of Carbon Storage in Three-River-Source National Park Based on PLUS-InVEST-Geodector Model

Exploring the spatial-temporal changes, driving forces, and future development tendency of the carbon sequestration service function of the Three-River-Source National Park ecosystem has great significance for regional ecological protection and sustainable development. Historical land-use data of Three-River-Source National Park from 1990 to 2020 were selected at five-year intervals, and based on the PLUS-InVEST-Geodector model, the spatial-temporal changes of historical carbon storage were analyzed, and the driving forces of spatial-temporal variation were explored combined with multiple factors. The carbon storage of Three-River-Source National Park in 2030 was predicted under the scenarios of natural development and ecological protection. The results showed that： ① The carbon storage of Three-River-Source National Park showed a fluctuating characteristic of increase-decrease-increase-decrease from 1990 to 2020, the carbon storage was increased by 41.85×106 t overall, and the grassland took the largest contribution. ② The spatial distribution characteristics of carbon storage in Three-River-Source National Park had little change between 1990 to 2020, and the evolution of the spatial distribution was relatively stable. The contribution ratio of the Yangtze River source park, Lancang River source park, and Yellow River source park was 7∶1∶2, which was roughly equivalent to the park area. ③ The major driving factors of the spatial-temporal variation of carbon storage in Three-River-Source National Park from 1990 to 2020 were： FVC, soil type, and annual precipitation. The interactive detection of each driving factor showed dual-factor enhancement and nonlinear enhancement. ④ The carbon storage of Three-River-Source National Park was predicted to decrease by 4.87% and 3.98% from 2020 to 2030 under the scenarios of natural development and ecological protection, respectively, and the carbon storage reduction under the ecological protection scenario had a significant inhibitory effect. The findings can provide data support for national spatial planning of Three-River-Source National Park and the enhancement of terrestrial ecosystem carbon storage.

Ultrasensitive Rapid Antigen Test by Geometric Lateral Flow Assays and Highly Doped Upconversion Nanoparticles.

The paper-based lateral flow assay (LFA) testing strips are currently the most widely used for point-of-care testing (POCT), valued for their rapid result turnaround times in a few minutes. However, their sensitivity has been limited. Upconversion nanoparticles (UCNPs), especially highly doped ones, have emerged as promising luminescent reporters to enhance the LFA sensitivity. These UCNPs exhibit a nonlinear enhancement in luminescence with excitation power density, necessitating higher power densities for higher brightness. In this study, we utilized a geometric paper strip design to minimize the immune reaction area and maximize the excitation power density, enabling ultrasensitive detection of the SARS-CoV-2 nucleoprotein antigen. This design also slowed the antigen flow on the paper strip, extending the reaction time between antigen and antibody, thereby enhancing the efficiency of the immune reaction. Through this design, our approach achieved over a 100-fold enhancement in the limit of detection (LOD) compared with the widely used LFAs, based on gold colloidal nanoparticles and europium nanoparticles. This innovation expands the scope of LFA applications that require a low LOD.

Illumination Correction and Enhancement Algorithm for Defective Powder Spreading Images in Selective Laser Sintering

Abstract Based on the uneven illumination distribution and low defect contrast in the powder spreading images acquired during the selective laser sintering manufacturing process, which affects subsequent defect detection, an adaptive nonlinear illumination correction and enhancement algorithm is proposed. Local illumination correction is performed using gray valueitting curves and is combined with global gamma correction to improve correction capability in overly dark areas. A nonlinear enhancement function is constructed to boost the contrast of the image’s illumination component. Finally, a gradient-based L0 norm minimization algorithm is used to smooth non-defect areas. Experimental results show that the proposed algorithm signi icantly improves correction and enhancement, outperforming comparison algorithms overall. Compared to the original images, the coef icient of variation decreases by an average of 0.179, and the standard deviation increases by an average of 26.7%, demonstrating the effectiveness of the proposed algorithm.

Deciphering laser shock peening quality monitoring: Wavelet-driven network with interpretability

Quality monitoring of laser shock peening based on acoustic emission technology is a topical multidisciplinary issue that has received much attention in recent years. To acquire complex time-varying information in acoustic emission signals, convolutional neural networks with powerful learning capabilities have shown potential for a wide range of applications. However, the black-box property of the network imposes limitations on its further development and decision credibility. Therefore, this study proposes a wavelet-driven network with theoretical basis and physical significance. This network can cyclically utilize discrete wavelet packet transform to map input features to the wavelet domain during the learning process, thereby obtaining more robust and valuable information. This paper also constructs a novel wavelet attention mechanism that takes into account the difference between low-frequency and high-frequency information, and is able to allocate resources in both the decomposition component and the time-domain dimension. The proposed method can be seen as a multiresolution analysis technique that combines existing physical knowledge with nonlinear feature processing and feature selective enhancement. The results of the two laser shock peening cases show that the proposed method not only outperforms current state-of-the-art models in terms of monitoring performance, but also has better physical interpretability. Importantly, the proposed method has the potential to be further extended to other interpretable structural health monitoring.

Norm constraints pyramid for image dehazing

Dark channel prior-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on the accuracy of the assumptions used in the target scenes, which incurs color distortion and brightness reduction when the models are used for real-world hazy images.We propose a norm constraints pyramid framework to improve the generalization performance of dehazing. First, a local color adaptive correction approach is devised to ascertain whether there is any color bias and thereafter rectify it automatically. Furthermore, multiple norm constraint methods are developed to improve the transmission and accomplish the first image removal. Finally, a non-linear enhancement method is created via this restriction that precisely modifies the brightness of an image.Through extensive experiments, we demonstrate that our framework establishes the new state-of- the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

Chemical Vapor Deposited Thin Palladium Sulfide Crystals for Highly Photoresponsive Photodetector

AbstractNonlayered palladium sulfide (PdS) is of interest due to its rich physical properties and promising applications in optoelectronic devices. However, the growth of thin nonlayered PdS remains challenging because of its intrinsic 3D lattice structure. Here, the first demonstration of the direct synthesis of thin rectangular PdS ribbons/flakes on SiO2/Si substrates by a facile chemical vapor deposition (CVD) approach is presented. The atomic structure and high crystalline quality of CVD‐derived PdS crystals are shown by scanning transmission electron microscopy. The nonlinear saturable absorption and absorption enhancement are revealed by using ultrafast optical pump‐probe spectroscopy, and the photocarrier dynamics present the hot phonon bottleneck and Auger recombination effects. Additionally, the Raman vibration modes display the polarization‐dependent properties verified by angle‐resolved polarized Raman spectroscopy. Importantly, the photodetector based on PdS ribbon demonstrates a decent photoresponsivity of ≈7.7 × 103 A W−1. This results provide an effective way to form thin nonlayered PdS with potential applications in the field of photodetection.

Theoretical reaction kinetics predictions for acetone and ketene with NH2 radicals: Implications on acetone/ammonia kinetic modeling

The cross-reaction kinetics of acetone/ketene (CH3COCH3/CH2CO) + amino (NH2) radicals are first theoretically reported for a wide range of conditions (T = 300–2500 K and P = 0.1–100 atm) in this work. The high-level electronic structure method CCSD(T)/cc-pVnZ(n = T, Q)//B3LYP-D3BJ/6–311++G(d,p) is used to explore the potential energy profiles on which the temperature- and pressure-dependence kinetic behaviors of the title reactions are characterized using the Rice-Ramsperger-Kassel-Marcus/Master Equation (RRKM/ME) theory. Corrections of the one-dimensional hindered rotor approximation and asymmetric Eckart tunneling effect are also included in the rate constant calculations. Furthermore, this work delves into the competitive relationships among the H-abstraction, NH2 addition and addition-dissociation reaction pathways. For CH3COCH3 + NH2, the H-abstraction reaction of CH3COCH3 is highly favored over the addition-dissociation reaction and other isomerization channels. For CH2CO + NH2, the reaction channel of addition-dissociation to form CH2NH2 + CO under low temperature conditions is more advantageous, while the H-abstraction reaction plays a dominant role under combustion conditions (T ≥ 1000 K). To reveal the impact of the studied reaction kinetics on model predictions, the rate constants of dominant reaction channels calculated in this work are incorporated into a kinetic model for the auto-ignition, oxidation and pyrolysis of CH3COCH3/NH3 mixtures. The simulated results show that the effect of the updated rate constants on ignition delay time is mainly at low temperatures and the promotion effect of CH3COCH3 addition on the ignition of NH3 presents a nonlinear enhancement. The updated rate constants can also accelerate the consumption of CH3COCH3 and CH2CO formation, while also influence the concentration distributions of other significant species (e.g., NH3, CO). Therefore, the cross-reaction kinetics of CH3COCH3/CH2CO + NH2 are critical in controlling the fuel consumption, important intermediate formation and ignition delay time of CH3COCH3/NH3 mixtures.

Spatio-temporal differentiation characteristics of coal mine safety levels in China based on spatial spillover effects

In order to solve the problem of "potpourri" of safety risk prevention and control measures, which is caused by the unclear mechanism of the spatial effect of coal mine safety level heterogeneity and its influencing factors. This paper discriminates the dominant factors of spatio-temporal heterogeneity of coal mine safety production level in China and their spatial effect types by means of GeoDetector and the Spatial Dubin Model (SDM), specifies the categories and degrees of acts on local and surrounding areas by changes in indicators, and provides further visualization of the detection outcomes with the assistance of the Neo4j graph database, and the findings indicate that:(1) All 15 indicators selected in the study have a certain influence on the generation of spatial heterogeneity in China's coal mine safety production level, and all of them show an enhancement relationship after the interaction of indicators. Especially, the combination of excavated environment and other indicators basically has a non-linear enhancement relationship. (2) In terms of spatial effects, the influence of the 5 effects on the spatio-temporal heterogeneity of coal mine safety level is, in descending order, industrial development effect > capital allocation effect > production environment effect > government supervision effect > enterprise management effect, which indicates that macroeconomic and market conditions have a much stronger influence on the generation of spatio-temporal heterogeneity in coal mine production safety status. (3) From the single indicator perspective, the average annual temperature, average annual wind speed, coal consumption and monitoring efficiency primarily affect the dependent variable through direct effects; GDP per capita, average labor compensation as well as railroad operating mileage have positive spatial spillover on the changes of coal mine safety production level in surrounding areas; the evaluation of the spatial effect for average labor compensation exhibits a positive indirect effect with low influence; for the two indicators of production efficiency and ex-factory price, not only do they have negative effects on the local coal mine safety level, but also have significant spillover effects on surrounding areas.

Photonic defect modes in cholesteric liquid crystal resonators with embedded isotropic layers

Photonic defect modes are explored as a viable alternative to standard photonic band edge modes in photonic crystal applications, especially due to their typically high Q-factors and local density of states. For example, they can be used in nonlinearity enhancement, lasing, and cavity quantum electrodynamics. However, they are strongly dependent on any structural change and need to be well-controlled to ensure the desired resonance frequency. Here, we present a study of the photonic defect modes that appear in a structure where a layer of isotropic material is embedded between two layers of cholesteric liquid crystal (CLC), using full electrodynamics numerical simulations. We present typical transmission spectra and electric field profiles of selected defect modes and then analyze the influence of geometrical and material parameters on the eigenfrequencies and Q-factors of the modes within and around the photonic bandgap, including refractive indices and thicknesses of isotropic and liquid crystal layers, and different anchoring orientations at the boundaries of the isotropic defect layer. Additionally, a connection of such defect modes to previously extensively analyzed twist defect modes is given. Eigenmodes in asymmetric resonators are also presented, where CLC layers surrounding the intermediate isotropic layer are not equally thick, enabling biasing of specific directional light emission. More generally, this work aims to contribute to the understanding and design capability in topological soft matter photonics where defect mode lasing could be realized in CLC geometries with different singular and solitonic topological defect structures.

NDVI Changes and Influencing Factors of Different Soil and Water Conservation Zones in Shandong Province

Exploring the characteristics of vegetation change and its influencing factors is essential to construct an ecological environment. Based on the NDVI dataset from 2000 to 2020, this study analyzed the spatial temporal attributes of NDVI changes in Shandong Province using the Sen trend analysis and the gravity center migration model. Furthermore, the spatial heterogeneity of NDVI and its influencing factors within the whole study area and different soil and water conservation zones were investigated using a Geo-detector model, considering population, hydrological, topographic, soil types, and vegetation types. The results were as follows： ① The NDVI in Shandong Province from 2000 to 2020 showed a fluctuating upward trend with significant seasonal characteristics that varied from different zones. The annual NDVI change showed a trend of single-peak in the Ⅲ-4-2t, Ⅲ-4-1xt, and Ⅲ-5-2w but showed a trend of double-peak in the Ⅲ-5-3fn. ② Regarding the spatial distribution, the NDVI was higher in the west-north and west-south areas and lower in the north and coastal areas. During the 21 years, the primary type of NDVI change was "medium-high coverage → high coverage," especially in the northeastern part of the soil conservation area of the Ⅲ-4-2t, the western part of the Ⅲ-4-1xt, and the ecological maintenance area of the Ⅲ-5-2w. Overall, 61.47% of the area had a positive trend of NDVI change with the gravity center of high coverage mitigating to the northeast, and the ecological environment was improved. ③ Soil types and population density were the dominant factors affecting NDVI in Shandong Province, with q values of 0.174 and 0.130, respectively. The chief factor in the Ⅲ-5-3fn, Ⅲ-4-2t, and Ⅲ-4-1xt was population density, with q values higher than 0.22, and the dominant factors in the Ⅲ-5-2w were soil types and vegetation types, with q values of 0.326 and 0.227, respectively. The interaction of the two factors enhanced the influence of the single factor, and the relationship between the influencing factors showed two-factor enhancement and nonlinear enhancement. The q-value of population density ∩ relative humidity was the highest, with a value of 0.257 in the Ⅲ-5-3fn. The q-value of population density ∩ soil types was the highest in the Ⅲ-4-2t and Ⅲ-4-1xt, reaching 0.297 and 0.378, respectively. The q-value of soil types ∩ vegetation types was the highest, with a value of 0.444 in the Ⅲ-5-2w. The results are expected to provide valuable references for improving the ecological environment of Shandong Province and lay a scientific foundation to make different conservation strategies for the individual soil and water conservation zones.

Source and Influencing Factor Analysis of Soil Heavy Metals Based on PMF Model and GeoDetector

In Lijiang City, as a typical example, 93 soil samples were collected from the study area, and soil pH； organic matter； and heavy metals arsenic （As）, mercury （Hg）, copper （Cu）, zinc （Zn）, lead （Pb）, cadmium （Cd）, and chromium （Cr） were determined. We explored the sources of heavy metals in the study area by means of Positive Definite Matrix Factorization （PMF） modeling and analyzed the impact of influencing factors by combining seven heavy metals with 13 influencing factors in a GeoDetector. The results showed that the mean values of soil heavy metals ω（As）, ω（Hg）, ω（Cu）, ω（Zn）, ω（Pb）, ω（Cd）, and ω（Cr） in the study area were 17.55, 0.19, 86.75, 164.84, 28.95, 0.39, and 167.87 mg·kg-1, respectively, which were greater than the background values of soils in Yunnan Province （except for As and Pb）. Regarding spatial distribution, the high values of Cu and Cr content were mainly concentrated in Yulong Naxi Autonomous County； the high value areas of As, Hg, Pb, and Cd were mainly concentrated in Ninglang Yi Autonomous County； and the high value of Zn content was mainly concentrated in Huaping County. Correlation analysis and PMF modeling revealed that the main sources of heavy metals As and Hg in the study area were industrial sources, Zn was from transportation pollution sources, Cr and Cu were from natural sources, and Cd and Pb were from agricultural sources. Further, the factor detector of the GeoDetector found that soil pH and organic matter （OC） had strong explanatory power for the content of seven heavy metals, and the interaction detector found that the results following the interaction of different influencing factors were nonlinear enhancement or two-factor enhancement, in which the interaction of OC and pH was the dominant factor for the spatial differentiation of heavy metals. This provides an important scientific basis for the protection of the soil environmental health and sustainable development in Lijiang City.

Metal contamination of the surface water and its effect on the higher gastric cancer incidence of Lujiang County, China

Metal contamination in surface water is one of the major environmental risk factors contributing to the incidence and mortality of gastric cancer (GC) in rural China. In this study, the generalized additive model (GAM) was used to explore the single and interactive effects of metal elements in surface water on GC incidence on the watershed scale in Lujiang County, east of China. Spatially, the areas with a high incidence of GC were concentrated in the northwest watershed where the drainage system was relatively developed. Apart from Sr, the average concentrations of Al, As, Be, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, and Zn in the northern watershed were higher than those in the southern watershed. Among them, the average concentrations of Be (0.036 mg/L), Fe (1.539 mg/L), Mn (0.355 mg/L), and Ni (0.040 mg/L) well exceeded China’s Grade III water quality standards. On the sub-watershed scale Be (F(5.130)), Pb (F(4.963)), As (F(3.508)), Cu (F(3.023)), Zn (F(2.922)), Cr (F(2.725)), Sn (F(2.588)), and Co (F(2.140)) in surface water were the main elements contributing to the high incidence of GC in Lujiang County, and the interaction effect mostly showed nonlinear enhancement. Especially, the nonlinear enhancement effect of Be with Cu (F(7.051)), Co (F(5.798)), Sn (F(5.328)), and Pb (F(5.193)) was more significant and had a greater effect on the incidence of GC. This study provides compelling direct evidence on the effects of metal contamination in surface water on the higher GC incidence in Lujiang County, China. It is recommended that there be routine monitoring of metal concentrations in surface water resources.

Spatial differentiation and influencing factors of effective phosphorus in cultivated soil in the water source area of the mid-route of South-to-North water transfer project.

The long-term application of phosphate fertilizers in agricultural production leads to a large accumulation of phosphorus in the soil. When it exceeds a certain limit, phosphorus will migrate to surrounding water bodies through surface runoff and other mechanisms, potentially causing environmental risks such as eutrophication of water bodies and increasing the risk of water source pollution. This study takes Shiyan City, the water resources area of the mid-route of the South-to-North Diversion Project (MSDP), as the study area. Based on 701 sampling points of topsoil, geostatistics and geodetectors were used to explore the spatial heterogeneity and influencing factors of available phosphorus (AP) in the topsoil of the area. The results show that the effective phosphorus content in the topsoil of the study area ranges from 0.30 to 146.00 mg/kg, with an average value of 14.28 mg/kg, showing strong variability characteristics. Geostatistical analysis shows that among all theoretical models, the exponential model has the best fitting effect, with a lump gold effect of 0.447 and a range of 82,000 m. The soil available phosphorus content shows an increasing trend from the Central Valley lowlands to the surrounding mountainous hills. Among them, elevation is the main controlling factor for the spatial variation of available phosphorus in the topsoil, followed by soil types, planting systems, annual precipitation, and organic matter. The non-linear enhancement or dual-factor enhancement among various environmental factors reveals the diversity and complexity of spatial heterogeneity affecting available phosphorus content in cultivated soil. This study could provide scientific references for maintaining ecological security in the water source area of the MSDP, improving the precise management of AP, and enhancing cultivated land quality.