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Background and aims: Amnestic mild cognitive impairment (aMCI) is the most common subtype of MCI, which carries a significantly high risk of transitioning to Alzheimer's disease. Recently, increasing attention has been given to remnant cholesterol (RC), a non-traditional and previously overlooked risk factor. The aim of this study was to explore the association between plasma RC levels and aMCI. Methods: Data were obtained from Brain Health Cognitive Management Team in Wuhan (https://hbtcm.66nao.com/admin). A total of 1,007 community-dwelling elders were recruited for this project. Based on ten tools including general demographic data, cognitive screening and some exclusion scales, these participants were divided into the aMCI (n = 401) and normal cognitive groups (n = 606). Physical examinations were conducted on all participants, with clinical indicators such as blood pressure, blood sugar, and blood lipids collected. Results: The aMCI group had significantly higher RC levels compared to the normal cognitive group (0.64 ± 0.431 vs. 0.52 ± 0.447 mmol/L, p<0.05). Binary logistics regression revealed that occupation (P<0.001, OR = 0.533, 95%CI: 0.423-0.673) and RC (p = 0.014, OR = 1.477, 95% CI:1.081-2.018) were associated factors for aMCI. Partial correlation analysis, after controlling for occupation, showed a significant negative correlation between RC levels and MoCA scores (r = 0.059, p = 0.046), as well as Naming scores (r = 0.070, p = 0.026). ROC curve analysis demonstrated that RC levels had an independent predictive efficacy in predicting aMCI (AUC = 0.580, 95%CI: 0.544 ~ 0.615, P <0.001). Conclusion: Higher RC levels were identified as an independent indicator for aMCI, particularly in the naming cognitive domain among older individuals. Further longitudinal studies are necessary to validate the predictive efficacy of RC. [ABSTRACT FROM AUTHOR]

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people's health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development. [ABSTRACT FROM AUTHOR]

Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg−1 in the plough layer (PL) and 13.92 g kg−1 in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg−1. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the spatio-temporal variation of soil nutrients as well as soil organic matter (SOM), and clarify the role of environmental and soil management factors in determining soil nutrients and SOM in farmland over Jiangxi Province of Southern China. Materials and methods: Between 2005 and 2012, we collected 16,504 surface soil samples (0–20 cm) from farmland across Jiangxi Province. Based on this soil dataset, we summarized the changes in SOM, alkali-hydrolyzable nitrogen (available N), available phosphorus (P), available potassium (K), pH, and cation exchange capacity. Then, we used the geostatistical method to explore and map the spatio-temporal variability of SOM, and available N, P, and K. Finally, the random forest algorithm was used to identify the main factors controlling the variation of SOM and available N, P, and K. Results and discussion: Our results revealed a clear right-skewed trend for the histogram of available P and K, pH, and cation exchange capacity in farmland soil of Jiangxi Province. From 2005 to 2012, the average concentrations of SOM and available P showed an insignificant decreasing temporal trend in the farmland of Jiangxi Province. The average concentrations of available N and available K showed a significant increasing trend between 2005 and 2012. In general, most of the soil samples had SOM, available N and P content at or above the level of Class 3 (high grade), and available K at or below the level of class 4 (moderate grade). The apparent lack of K fertility was detected. Regarding the spatio-temporal variation pattern, noticeable changes in the concentrations of SOM and available N, P, and K were detected in most of the region in 2012 when compared with 2005. Conclusion: Farmland soils in Jiangxi Province had good fertility, and soil nutrients and SOM in farmland showed strong spatial variability. Overall, the climate (e.g., mean annual precipitation and mean annual temperature) and soil management (e.g., straw return and chemical fertilizer application) had dominant effects on soil nutrients and SOM, while other factors such as relief and soil properties had slight effect. The straw return is a sustainable way to improve soil fertility. Moreover, soil pH has a slight impact on soil nutrients and SOM. Great efforts are needed to prevent farmland soils from further acidification. [ABSTRACT FROM AUTHOR]

Since entering the new century, People’s living standards are constantly improving, with the continuous improvement of living conditions, people are becoming more and more important in education, which is the embodiment of the enhancement of national strength. The education level is getting higher and higher, and a good education level needs a good teacher–student relationship. To solve these problems, we use the emotional cognition of God’s network to study the teacher–student relationship, and collect and analyze the data of the teacher–student relationship. In this chapter, we use GABP neural network algorithm DHNN algorithm and discrete Hopfield neural network to make the collected data more convenient to be analyzed. The research shows that there is a close relationship between the educational level and the relationship between teachers and students in China, and a good relationship between teachers and students will promote the improvement of educational level. According to the research data, “face-to-face” is the most important way of interaction between tutors and postgraduates in various types of teacher– student relationship. QQ WeChat is also one of the main ways of interaction between students and teachers, which shows that the interaction between students and teachers is talking about the interaction between online and Internet. The education industry is becoming more and more important, and the teacher–student relationship is the most important part of the education industry. Good teacher–student relationship is helpful to cultivate students’ healthy personality. In view of the cold relationship between teachers and students at present, we need to make some measures the relationship between teachers and students and effectively use the relationship between teachers and students to promote the better development of the education industry. [ABSTRACT FROM AUTHOR]

Calibration and reconstruction of time series DMSP-OLS nighttime light images are critical for understanding urbanization processes and the evolution of urban spatial patterns from a unique perspective. In this study, we developed an improved stepwise calibration (ISC) method based on numerical constancy to correct and reconstruct the time series of China's regional nighttime light data, thus eliminating the drawbacks of the invariant target region method. We evaluated the different calibration methods and quantitatively validated the calibrated nighttime light data using gross domestic product (GDP) and electricity consumption (EC) at municipal, provincial, and national scales. The results indicated that the ISC method demonstrated its advantage in screening stable lit pixels and maintaining the temporal variability of multi-year nighttime light variation. The variation curve of reconstructed multi-year nighttime light obtained by the ISC method based on numerical constancy was more consistent with the actual urban development. The ISC method retained the original data's most abundant and complete information than other calibration methods. Moreover, the significant advantages of this method in the low-light high-variation regions and high-light low-variation regions offered new possibilities for understanding the development of small- and medium-sized nighttime light centers such as towns and villages from a nighttime light perspective. This is an advantage that other calibration methods do not offer. The correlation between the multi-year nighttime light dataset obtained by the ISC method and the socio-economic data was significantly improved. The correlation coefficients with GDP and EC are 0.9695 and 0.9923, respectively. Last but not least, the ISC method is more straightforward to implement. The new framework developed in this study produces a more accurate and reliable long time series nighttime light dataset and provides quality assurance for subsequent research in socio-economic development, urban development, natural disasters, and other fields. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) plays an important role in the global carbon cycle and soil fertility supply. Rapid and accurate estimation of SOC content could provide critical information for crop production, soil management and soil carbon pool regulation. Many researchers have confirmed the feasibility and great potential of visible and near-infrared (Vis-NIR) spectroscopy in evaluating SOC content rapidly and accurately. Here, to evaluate the feasibility of different spectral bands variable selection methods for SOC prediction, we collected a total of 330 surface soil samples from the cotton field in the Alar Reclamation area in the southern part of Xinjiang, which is located in the arid region of northwest China. Then, we estimated the SOC content using laboratory Vis-NIR spectral. The Particle Swarm optimization (PSO), Competitive adaptive reweighted sampling (CARS) and Ant colony optimization (ACO) were adopted to select SOC feature bands. The partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) inversion models were constructed by using full-bands (400–2400 nm) spectra (R) and feature bands, respectively. And we also analyzed the effects of spectral feature band selection methods and modeling methods on the prediction accuracy of SOC. The results indicated that: (1) There are significant differences in the feature bands selected using different methods. The feature bands selected methods substantially reduced the spectral variable dimensionality and model complexity. The models built by the feature bands selected by CARS, PSO and ACO methods showed the different potential of improvement in model accuracy compared with the full-band models. (2) The CNN model had the best performance for predicting SOC. The R2 of the optimal CNN model is 0.90 in the validation, which was improved by 0.05 and 0.04 in comparison with the PLSR and RF model, respectively. (3) The highest prediction accuracy was archived by the CNN model using the feature bands selected by CARS (validation set R2 = 0.90, RMSE = 0.97 g kg−1, RPD = 3.18, RPIQ = 3.11). This study indicated that using the CARS method to select spectral feature bands, combined with the CNN modeling method can well predict SOC content with higher accuracy. [ABSTRACT FROM AUTHOR]

Accurate updating of soil salination and alkalization maps based on remote sensing images and machining learning methods plays an essential role in food security, biodiversity, and desertification. However, there is still a lack of research on using machine learning, especially one-dimensional convolutional neural networks (CNN)s, and soil-forming factors to classify the salinization and alkalization degree. As a case study, the study estimated the soil salination and alkalization by Random forests (RF) and CNN based on the 88 observations and 16 environmental covariates in Da'an city, China. The results show that: the RF model (accuracy = 0.67, precision = 0.67 for soil salination) with the synthetic minority oversampling technique performed better than CNN. Salinity and vegetation spectral indexes played the most crucial roles in soil salinization and alkalinization estimation in Songnen Plain. The spatial distribution derived from the RF model shows that from the 1980s to 2021, soil salinization and alkalization areas increased at an annual rate of 1.40% and 0.86%, respectively, and the size of very high salinization and alkalization was expanding. The degree and change rate of soil salinization and alkalization under various land-use types followed mash > salinate soil > grassland > dry land and forest. This study provides a reference for rapid mapping, evaluating, and managing soil salinization and alkalization in arid areas. [ABSTRACT FROM AUTHOR]

Widespread soil acidification due to atmospheric acid deposition and agricultural fertilization may greatly accelerate soil carbonate dissolution and CO2 release. However, to date, few studies have addressed these processes. Here, we use meta-analysis and nationwide-survey datasets to investigate changes in soil inorganic carbon (SIC) stocks in China. We observe an overall decrease in SIC stocks in topsoil (0–30 cm) (11.33 g C m–2 yr–1) from the 1980s to the 2010s. Total SIC stocks have decreased by ∼8.99 ± 2.24% (1.37 ± 0.37 Pg C). The average SIC losses across China (0.046 Pg C yr–1) and in cropland (0.016 Pg C yr–1) account for ∼17.6%–24.0% of the terrestrial C sink and 57.1% of the soil organic carbon sink in cropland, respectively. Nitrogen deposition and climate change have profound influences on SIC cycling. We estimate that ∼19.12%–19.47% of SIC stocks will be further lost by 2100. The consumption of SIC may offset a large portion of global efforts aimed at ecosystem carbon sequestration, which emphasizes the importance of achieving a better understanding of the indirect coupling mechanisms of nitrogen and carbon cycling and of effective countermeasures to minimize SIC loss. [ABSTRACT FROM AUTHOR]

The source identification and apportionment of heavy metals (HMs) is a vital issue for restoring contaminated soil. In this study, qualitative approaches [a finite mixture distribution model (FMDM) and raster-based principal components analysis (RB-PCA)] and a quantitative approach [positive matrix factorization (PMF)] were composed to identify and apportion the sources of five HMs (Cd, Hg, As, Pb, Cr) in Wenzhou City, China, using several crucial auxiliary variables. An initial ecological risk assessment suggested that the ecological risk level in the study area was generally considered low, with the greatest contamination contributions coming from Cd and Hg. The result of the FMDM showed that Cd and Pb fit a single log-normal distribution, Hg fit a double log-normal mixed distribution, and As and Cr presented a triple log-normal distribution. Each element was identified and separated from its natural or anthropogenic sources. A map of RB-PCA combined with an analysis of corresponding auxiliary variables suggested that the three main contribution sources in the entire study area were parental materials, industrial and agricultural mixed pollution, and mining exploration activities. Each element was discussed, using the PMF model, with regard to its quantitative contributions. Parental materials contributed to all elements (Cd, Hg, As, Pb, Cr) at 89.22%, 7.31%, 35.84%, 84.81% and 27.42%, respectively. Industrial emissions and agricultural inputs mixed pollution contributed 2.94%, 80.77%, 15.93%, 4.79%, and 25.63%, respectively. Mining activities contributed 7.84%,11.92%, 48.23%, 10.40% and 46.95%, respectively, to the five HMs. Such result could be used efficiently to generate scientific decisions and strategies in terms of decision-making on regulating HM pollution in soils. [ABSTRACT FROM AUTHOR]

Large deformation has always been a focus and difficult issue in the construction of deep-buried tunnels in squeezing rock. Previous studies mainly focused on the large deformation of medium and small span railway/highway tunnels in soft ground. However, there are limited researches on the large deformation control methods for large-span (three-lane) highway tunnels constructed in unfavorable geological environment. Based on the Lianchengshan Tunnel of the Baoji-Hanzhong expressway in Shaanxi Province, China, this paper studied the deformation behaviors and mechanical mechanisms of a large-span tunnel excavated in chlorite schist formation with single primary lining method and double primary lining method by in-situ test and numerical simulation. The achieved results indicate that the double primary lining method is much more effective than that of the single primary lining method in restraining the deformation of surrounding rock, and the maximum vertical displacement and horizontal convergence are reduced by 67% and 66%, respectively. The support method of double HK200b-type steel sets combined with large-diameter foot reinforcement bolt (FRB) and deep invert could effectively control the large deformation of the case tunnel, which effectively avoided the supporting structure failure, repeated clearance invasion and multiple reshaping work caused by the single primary lining method and conformed to the energy-saving construction concept of "no clearance interfering, no support reshaping" of tunnels in squeezing ground. Simulation analysis of surrounding rock deformation, supporting structure stress and plastic zone distribution was performed to evaluate the support effect of the two deformation-controlled methods. Finally, the deformation and stress characteristic curves of rock-support of the two deformation-controlled methods were established, which revealed the supporting mechanism of double primary linings for large-span tunnels in chlorite schist. The research results can provide a theoretical basis and practical reference for the large-deformation control of similar large-span tunnels in squeezing rock. [ABSTRACT FROM AUTHOR]

Environmental pollution problems and energy-saving issues of urban tunnels have increasingly been attracting people's attention. The paper aims at investigating a reasonable time factor for CO-based emission factors in the ventilation design of urban road tunnels. The study results show that the prediction curve of CO-based emission factor for passenger cars consists of two stages. The average reduction rates of Stage I (2004–2010) and Stage II (2010–2030) are 38% and 14% per year, respectively. The CO emission factor series of passenger cars conforms to exponential function distribution. The time factors of CO-based emission backcalculated from the measured data range from 4.9% to 12.6%, which are 2.4–6.3 times the specified value in the Chinese ventilation specification but slightly smaller than predicted ones. Based on four aspects of predicted results, back analysis results, related references, and engineering applications, it is indicated that the time factor of CO-based emission factor (2%) in the current ventilation specification is conservative. And when defining 2000 and 2010 as base years, respectively, 4% and 3% can be used as the time factors of CO-based emission factors respectively for the ventilation design of urban tunnels. This study can provide a reference for ventilation design of urban tunnels in other regions. [ABSTRACT FROM AUTHOR]

The number of super-large-span tunnels is increasing in both new construction and reconstruction projects in China recently. In super-large-span tunneling engineering, the deformation properties and mechanical behaviors of tunnel portal structure are more complex than those of common tunnel due to the flatter shape and larger construction span. The mechanical behaviors of rock mass change in response to different sequential excavation methods and supporting parameters. The upper bench CD method has been gradually applied in the construction of super-large-span tunnels in China. In this paper, the design parameters for the supporting structure of super-large-span tunnel were studied by the field monitoring and numerical modeling in a case study of Laohushan tunnel. It was found that the crown settlement was larger than the clearance convergence, and the stress of arch was greater than that of the side wall in tunnel portal section. The invert structure was flat with small curvature. Therefore, the shotcrete was mainly subjected to tensile stress. The use of H200 × 200 steel rib with spacing of 60 cm and C25 shotcrete with thickness of 30 cm is recommended. The results of this paper provide basis for the development of design specifications and construction standards for super-large-span tunnels and provide reference for similar projects in the future. [ABSTRACT FROM AUTHOR]

In the new construction or reconstruction of expressway projects, the number of highway twin tunnels with eight lanes is increasing. However, there are no corresponding design support parameters and measures in the current technical specifications for tunnel design and construction in China. In Laohushan superlarge-span highway tunnel with single hole and four lanes, the deformation behavior and mechanical characteristics of support structures are measured and analyzed. The monitoring results indicated that the deformation of tunnel structure mainly experienced three stages: rapid deformation, slow deformation, and stable deformation, and finally reached a relatively stable state; the structure stress of primary support and secondary lining increases sharply at first and then tends to be stable gradually with the gradual construction of each excavation part in the tunnel; the stress of each measuring point at the steel rib is less than the yield limit of steel rib (235 MPa), and the support structure is safe and stable in the process of tunnel construction. Then, the structure safety of primary support under different support parameters is simulated and calculated by numerical simulation with Grade IV rock mass, and the reasonable support parameters for Laohushan highway tunnel are studied considering the structural safety and engineering economy. It is suggested to use the H175-type steel sets with a distance of 80 cm and C25 shotcrete with a thickness of 26 cm. The results could provide reference for similar tunnel projects and provide a basis for the design specification and construction standards for superlarge-span tunnels. [ABSTRACT FROM AUTHOR]

Kiwifruit bacterial canker (KBC) caused by Pseudomonas syringae pv. actinidiae (Psa) is a worldwide epidemic. Forty-seven strains of Psa in Biovar 3 from different geographic regions in the world, thirty-seven of them collected in China, were selected to study genetic variation by using Rep-, RAPD- and IS50- PCR assays. Twenty-two strains of Chinese Psa were selected to test the fluorescence of each strain when grown on King's B (KB) medium and examined under UV light, the pathogenicity of seven representative strains of Chinese Psa isolated from different provinces or cultivars was tested by the foliage spray inoculation of three cultivars with different tolerances to KBC. Twenty-eight of the main cultivars were subjected to a tolerance assay by wound inoculation on vines in vivo. The results showed that Chinese Psa Biovar 3 strains have a broad genetic variability related to their different geographic regions, Chinese Psa Biovar 3 strains had faint to brighter fluorescence (ΔE = 0.25–1.00, GFI = 1–3), although they were suggested to lack fluorescence in the past. Schy4 and JF8 were the most virulent; GC40, Hwd1 and Sh13 were the least virulent; and Jzhy2 and Jzzm2 were moderately virulent among the strains tested in this study. According to the combined data of necrosis length when inoculated with each of the three Chinese Psa strains (Schy4, Hwd1 and Jzhy2), the tolerance of the 28 tested cultivars to KBC could be divided into three grades. In conclusion, there were significant differences in the fluorescence and pathogenicity of Chinese Psa strains, but the fluorescence emission is not correlated with the virulence and pathogenicity of strains in this study. It was proposed that such cultivars as 'Wanjin', 'Donghong', 'Huate', 'Fujianmaohua', 'Ganmi No. 6', 'Hongguoruanzao', 'Ruanzao No. 2' and 'Jialv' kiwifruit could be used as parental materials to breed new cultivars tolerant to KBC in future kiwifruit production. [ABSTRACT FROM AUTHOR]

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and four integrated industry types by five different machine learning approaches. Multinomial naive Bayesian (NB) methods achieved an accuracy of 87% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 260,000 enterprises. The relationship between the different industry classes and measurements of soil cadmium (Cd) and mercury (Hg) concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of Cd, elevated concentrations also occurred in some areas because of excessive fertilization and coal mining. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites. Image 1 • Potentially polluting enterprises are identified and classified. • Spatial correlation of Cd and Hg with polluting enterprises is observed. • Industry pollution affects the distribution of Cd and Hg pollution in soils. • High contents of Cd occur in some areas due to fertilization and coal mining. • Hg pollution caused by chemical industry is more serious than other industries. Capsule: This work provides a new way based on machine learning methods using geographic dataset for pollution source identification and risk mitigation. [ABSTRACT FROM AUTHOR]

• A locally modelling approach was proposed to build spectral model for estimating leaf nutrients at regional scale. • This approach considered the varied relations between leaf nutrients and leaf spectra over space. • This approach was applied to a case study in Hainan Island to estimate leaf phosphorus concentration of rubber trees. • The proposed approach outperformed the commonly used globally modelling method. The globally modelling method was commonly used to build spectral models for estimating leaf nutrients of plants. However, predictive ability of such models was rather limited at regional scale because of the unreasonable assumption inherent in the globally modelling method which assumed that relationships between leaf nutrients and leaf spectra were stable over space. In fact, the leaf nutrients-spectra relationships generally varied across extensive area due to influences of various environmental factors. In order to accommodate the varied leaf nutrients-spectra relationships at regional scale, the locally modelling approach was proposed to develop spectral models for predicting leaf nutrients. The locally modelling approach assumed that the leaf nutrients-spectra relationships were stable at local spaces within which environmental conditions were relatively uniform. According to this basic idea, the proposed approach used environmental factor as grouping variable to classify leaf samples into several homogenous sub-datasets. Within these sub-datasets, the leaf nutrients-spectra relationships would be more stable. Then, linear or non-linear method were employed to fit model for each sub-dataset. In order to verify the effectiveness of this method, the locally modelling approach was applied to a case study to estimate leaf phosphorus concentration (LPC) of rubber trees (Hevea brasiliensis) in Hainan Island, China. In this case study, three scenarios were set up. In the first scenario, the local model was built using the partial least squares regression (PLSR) with full wavelengths as input variables. In the second scenario, the local model was also developed using the PLSR but with manually selected wavelengths as input variables. In the third scenario, the local model was constructed using the artificial neural networks with feature bands as input variables. Performances of these local models were compared to that of the corresponding global models. Results showed that for the three scenarios local models always yielded higher prediction accuracies than that of the global models in the test dataset. For the first scenario, values of coefficient of determination (r2) and root mean square error (RMSE) ranged from 0.652 to 0.735 and from 0.030% to 0.034%, respectively, for the local model, whereas those varied from 0.458 to 0.637 and from 0.037% to 0.041%, respectively, for the global model. For the second scenario, values of r2 and RMSE ranging from 0.565 to 0.734 and from 0.031% to 0.046%, respectively, for the local model, while those varied from 0.275 to 0.559 and from 0.038% to 0.053%, respectively, for the global model. For the third scenario, values of r2 and RMSE ranging from 0.678 to 0.863 and from 0.021% to 0.033%, respectively, for the local model, while those varied from 0.441 to 0.726 and from 0.030% to 0.043%, respectively, for the global model. This result demonstrates that the locally modelling approach was more effective in estimating LPC of rubber trees than the globally modelling method at the regional scale. In the future, more environmental factors should be considered and their combined effects on leaf sample classification need to be investigated. [ABSTRACT FROM AUTHOR]

In the recent past, much of the marshland in China has been reclaimed for agriculture and to create urban buffer zones. Information about the variability of soil properties (e.g. salinity) in these reclaimed areas is mostly limited, but is crucial for improving agricultural production as well as sustaining their environmental benefits. Characterization of variations in soil salinity and their correlation with other soil properties at different scales can improve the use of these reclaimed areas, as well as assess the efficacy of the reclamation process. In this study, we examined variations in soil salinity at different directions using anisotropic analysis (AA) and separated the variations at different scales using empirical mode decomposition (EMD). The AA identified two pairs of major and minor axes along which variations in soil salinity were dominant. The EMD separated the variations in soil salinity along these axes into six intrinsic mode functions according to the dominant scales of variations. The importance of these scale components were assessed based on their percent variance contribution towards the overall variance. Large variations in soil salinity were observed mainly at 200–500 m and 3500–4500 m scales. The scale-specific variations in soil salinity and their correlation with other soil properties were direction-specific. A strong correlation between the large scale components of soil salinity (> 5 km) and soil total nitrogen and clay content were identified along the directions from inland to the ocean. This may be attributed to the distance from the ocean as well as the length of time for which reclaimed land has been cultivated. Information about this variability at different scales and directions acquired by combining AA and EMD provided a unique way to characterize reclaimed lands for agricultural or environmental use. This information will be helpful in developing future sampling strategies and preparing detailed digital soil maps for improved management decisions. [ABSTRACT FROM AUTHOR]

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles. [ABSTRACT FROM AUTHOR]

Soil visible-near infrared diffuse reflectance spectroscopy (vis-NIR DRS) has become an important area of research in the fields of remote and proximal soil sensing. The technique is considered to be particularly useful for acquiring data for soil digital mapping, precision agriculture and soil survey. In this study, 1581 soil samples were collected from 14 provinces in China, including Tibet, Xinjiang, Heilongjiang, and Hainan. The samples represent 16 soil groups of the Genetic Soil Classification of China. After air-drying and sieving, the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer. All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses. The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification. The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils. The results on the classification of the spectra are comparable to the results of other similar research. Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression (PLSR). This combination significantly improved the predictions of soil organic matter ( R = 0.899; RPD = 3.158) compared with using PLSR alone ( R = 0.697; RPD = 1.817). [ABSTRACT FROM AUTHOR]

The article reports on research which investigated the effect that mouth only breathing had on time to completion, heart rate, rate of perceived exertion, blood lactate, and ventilatory measures during a high-intensity shuttle run sequence. Researchers evaluated ten subjects. They found that the mouth breathing only impacted heart rate and that its use was questionable for training purposes.

The Loess Plateau located in the upper and middle reaches of the Yellow River is the most serious soil erosion region in China. Check-dams (also known as silt trappers) have been demonstrated to be an effective soil and water conservation measure in the region and a traditional workflow for check-dam planning and engineering is time-consuming and cannot meet the requirement of efficiently locating optimal check-dam sites. Fine resolution satellite imagery and analysis can play a key role in screening and determining special river bed segments that can be candidates for check-dam sites. In this research, HongShiMao watershed of the Loess Plateau was selected as our case study area. Based on a detailed analysis of spectral characteristics of a fused SPOT-5 imagery for dominant land covers and geomorphological features of a constructed digital elevation model, the river bed of key channels within the watershed was automatically identified. Then we selected four check-dam sites on the river bed and four orientations of a check-dam site to explore locational and directional profiles. Such profile information is most useful for locating optimal check-dam sites in a cost-effective manner and reducing associated expenditures surrounding check-dam constructions. This remote sensing application demonstrates the latest spatial information technologies such as fine resolution satellite imagery and 3D geospatial visualisation hold promises for changing traditional workflows and advancing scientific decision making of environmental conservation projects. [ABSTRACT FROM AUTHOR]

Land use intensification increases crop production without reclaiming new cropland from natural ecosystems. In addition to the yield-improving approaches, scientists began to assess the multiple cropping potential as an alternative for intensification. These existing assessments largely focused on the number of multiple cropping frequency (MCF) while ignoring the crop growth duration (GD) required by a complete cropping circle, which may bring uncertainty on the estimates. In this study, we aim to address such uncertainty by comprehensively considering the relation between MCF and GD. Selecting Jinxian county, Jiangxi Province, as a study area, we use time-series remote sensing imageries to observe MCF and GD, and to estimate the region's annual growing season (GS) by aggregating the crop-specific GDs corresponding to the MCF. We further apply scenario analysis to understand how crop choice. i.e. longer GD v.s. shorter GD, would influence the exploitation of GS and thus the multiple cropping potential. Results show that without considering GD, 33.63% croplands have the potential to harvest another time and 64.84% cropland could have two-extra harvests, which would result in the harvested area increased by + 163.31%. However, if GD is considered, this potential is estimated much lower in overall and is varied greatly across crop selection scenarios. In the most close-to-reality scenario, roughly 40% croplands have no potential to make extra harvest, because the GS in many areas have been largely exploited and the remaining GS is insufficient to sustain another complete crop growth. Our study shows that crop choice plays an important role in measuring and exploiting the multiple cropping potential, which provides implications for China's "agricultural supply-side structural reform" through sustainable land use intensification. ● Multiple cropping potential is the maximum intensified cropland use. ● The duration for two shorter-season crops might equals to one longer-season crop. ● Crop selection schemes are applied to exploit multiple cropping potential. ● Crop choice is the important role for assessing land use intensification. ● Cropland requires further sustainable land use management. [ABSTRACT FROM AUTHOR]

Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas. This study developed a framework for the use of the Vis–NIR spectroscopy to diagnose soil Cd contamination in urban and suburban soils. [ABSTRACT FROM AUTHOR]

Abstract: Heavy metal concentrations in agricultural soils of Zhejiang Province were monitored to indicate the status of heavy metal contamination and assess environmental quality of agricultural soils. A total of 908 soil samples were collected from 38 counties in Zhejiang Province and eight heavy metal (Cd, Cr, Pb, Hg, Cu, Zn, Ni and As) concentrations had been evaluated in agricultural soil. It was found 775 samples were unpolluted and 133 samples were slightly polluted and more respectively, that is approximately 14.65% agricultural soil samples had the heavy metal concentration above the threshold level in this province by means of Nemerow''s synthetical pollution index method according to the second grade of Standards for Soil Environmental Quality of China (GB15618-1995). Contamination of Cd was the highest, followed by Ni, As and Zn were lower correspondingly. Moreover, Inverse Distance Weighted (IDW) interpolation method was used to make an assessment map of soil environmental quality based on the Nemerow''s pollution index and the soil environmental quality was categorized into five grades. Moreover, ten indices were calculated as input parameters for principal component analysis (PCA) and the principal components (PCs) were created to compare environmental quality of different soils and regions. The results revealed that environmental quality of tea soils was better than that of paddy soils, vegetable soils and fruit soils. This study indicated that GIS combined with multivariate statistical approaches proved to be effective and powerful tool in the mapping of soil contamination distribution and the assessment of soil environmental quality on provincial scale, which is beneficial to environmental protection and management decision-making by local government. [Copyright &y& Elsevier]

Potentially toxic element (PTE) pollution has been extensively studied at a local and regional scale in China. However, further research needs to be conducted at a national level. To this end, in current study we systematically compiled data of around 170,000 soil samples collected from 1153 papers published between 2008 and 2018. Based on these data we conducted a comprehensive analysis on the pollution status, pollution hotspots, and potential dominant sources of PTEs (As, Cd, Cr, Cu, Hg, Pb, Ni and Zn) in soils in 271 cities of China using geochemical accumulation index, potential ecological risk index, health risk evaluation model, univariate local Moran's I index, and bivariate local Moran's I index. Our results indicated an obvious accumulation of PTEs in the soils of most cities. In addition, the contents of Cd, Hg, Pb, and Ni were higher in China when compared to other several countries under comparison. Pollution hotspots of PTE and hotspots of human health risks may occur due to PTE exposure were mainly distributed in South (S) and Southwest (SW) of China. Cities with PTEs accumulation in soil due to industrial activities were mainly located in East (E) and North (N) China. Cities that had high concentrations of PTE due to agricultural activities were mainly located in central and Northeast (NE) China. Most cities with an accumulation of PTEs in soils primarily due to mining activities were found in West (W) and Northwest (N) China. Cities with PTEs mainly sourced from soil parental material were distributed in Southwest (SW) China. This study provides comprehensive and specific information and valuable implications for developing advanced scientific and efficient strategies to prevent and control PTE pollution the soils in China. Image 1 • An obvious accumulation of PTEs was detected in the soils of most cities in China. • Cd and Hg were confirmed as most seriously polluted elements in soil in China. • Contents of Cd, Hg, Pb, and Ni were higher in China as compared to other countries. • Residents in south and southwest China suffer greater human health risk. • Dominant sources of PTEs in soil in different cities in China were identified. Different sources which contributed potentially toxic element accumulation in different cities in China were identified in this study. [ABSTRACT FROM AUTHOR]

In this study we systematically reviewed 1203 research papers published between 2008 and 2018 in China and recorded related data on eight kinds of soil heavy metals (Cr, Pb, Cd, Hg, As, Cu, Zn, and Ni). Based on that, the pollution levels, ecological risk and health risk caused by soil heavy metals were evaluated and the pollution hot spots and potential driving factors of different heavy metals in different provinces were also identified. Results indicated accumulation of heavy metals in soils of most provinces in China compared with background values. Consistent with previous findings, the most prevalent polluted heavy metals were Cd and Hg. Polluted regions are mainly located in central, southern and southwestern China. Hunan, Guangxi, Yunnan, and Guangdong provinces were the most polluted provinces. For the potential health risk caused by heavy metals pollution, children are more likely confront with non-carcinogenic risk than adults and seniors. And children in Hunan and Guangxi province were experiencing relatively larger non-carcinogenic risk. In addition, children in part of provinces were undergoing potentially carcinogenic risks due to soil heavy metals exposure. Furthermore, in our study the 31 provinces in mainland China were divided into six subsets according to corresponding potential driving factors for heavy metal accumulation. Our study provide more comprehensive and updated information for contributing to better soil management, soil remediation, and soil contamination control in China. Image 1 • Cd and Hg were the most polluted heavy metals in soil across China. • The ER values for Cd in 30 provinces varied from moderate to very high risk. • Hunan, Guangxi, Yunnan and Guangdong were confirmed as priority control provinces. • Children are undergo larger health risk than adults and seniors in China. • Controlling factors of heavy metals accumulation in different provinces were mapped. [ABSTRACT FROM AUTHOR]

Agricultural pollution poses a considerable challenge to grain security and human health, especially in economically developed areas. Mineral exploitation, chemical enterprise operation, pesticide and fertilizer application, sewage discharge, and vehicle emissions are the pollution sources of agricultural land. Identifying and assessing potential agricultural pollution (PAP) is, therefore, the most urgent task to achieve grain security and the human health. Large-scale (e.g., regional or national) PAP assessment can be very expensive, which could also generate a certain amount of information that usually discourages evaluation by decision-makers. To identify areas for regional priority investigation, here we proposed an assessment framework for PAP in economically developed areas. The framework consisted of PAP assessment, vulnerability assessment, hazard assessment, and socio-economic assessment. Then, we conducted a case study by using the proposed framework in one of China's economically developed areas, Zhejiang Province. The results showed that PAP, especially soil heavy metal pollution, soil acidification, and surface water pollution involved almost the entire study area. High-vulnerability high-hazard areas were mainly associated with high socio-economic development or high grain yield. These areas had negatively affected grain security and increased carcinogenic risk, potentially contributing to the formation of cancer villages. Based on the results, we proposed measures for environmental risk managers to alleviate the impact of PAP on grain security and human health in economically developed areas. Image 1 • A new assessment framework for potential agricultural pollution (PAP) was proposed. • Exposure to PAP impacted grain security and human health. • Hazard of mines and chemical companies in economically developed areas were highlighted. • The distribution of centers of grain production shifted toward PAP areas in Zhejiang. The proposed assessment framework can effectively assess priority investigation areas of PAP in China's economically developed areas. [ABSTRACT FROM AUTHOR]

The prediction and identification of the factors controlling heavy metal transfer in soil-crop ecosystems are of critical importance. In this study, random forest (RF), gradient boosted machine (GBM), and generalised linear (GLM) models were compared after being used to model and identify prior factors that affect the transfer of heavy metals (HMs) in soil-crop systems in the Yangtze River Delta, China, based on 13 covariates with 1822 pairs of soil-crop samples. The mean bioaccumulation factors (BAFs) for all crops followed the order Cd > Zn > As > Cu > Ni > Hg > Cr > Pb. The RF model showed the best prediction ability for the BAFs of HMs in soil-crop ecosystems, followed by GBM and GLM. The R2 values of the RF models for the BAFs of Zn, Cu, Cr, Ni, Hg, Cd, As, and Pb were 0.84, 0.66, 0.59, 0.58, 0.58, 0.51, 0.30, and 0.17, respectively. The primary controlling factor in soil-to-crop transfer of all HMs under study was plant type, followed by soil heavy metal content and soil organic materials. The model used herein could be used to assist the prediction of heavy metal contents in crops based on heavy metal contents in soil and other covariates, and can significantly reduce the cost, labour, and time requirements involved with laboratory analysis. It can also be used to quantify the importance of variables and identify potential control factors in heavy metal bioaccumulation in soil-crop ecosystems. Image 1 • Three methods were used to model accumulation of heavy metals in soil-crop system. • Random forest performed best on modelling heavy metals transport in soil-crop system. • The model performed best on Zn (R2 = 0.84) followed by Cu, Cr, Ni, Hg, Cd, As, Pb. • Plant type was the primary control of heavy metals transport in soil-crop system. Bioaccumulation efficient of HMs in soil-crop system was estimated and related controlling factors were also identified using machine learning algorithm. [ABSTRACT FROM AUTHOR]

Accurate estimates of the spatial distribution of total nitrogen (TN) in soil are fundamental for soil quality assessment, decision making in land management, and global nitrogen cycle modeling. In China, current maps are limited to individual regions or are of coarse resolution. In this study, we compiled a new 90-m resolution map of soil TN in China by the weighted summation of random forest and extreme gradient boosting. After harmonizing soil data from 4022 soil profiles into a fixed soil depth (0–20 cm) by equal area spline, 18 environmental covariates were employed to characterize the spatial pattern of soil TN in topsoil across China. The accuracy assessments from independent validation data showed that the weighted model averaging gave the best predictions with an acceptable R2 (0.41). The prediction map showed that high-value areas of soil TN were mainly distributed in the eastern Tibetan Plateau, central Qilian Mountains and the north of the Greater Khingan Range. Climate factors had a considerable influence on the variation of the soil TN, and land-use types played a pivotal part in each climate zone. This high-resolution and high-quality soil TN data set in China can be very useful for future inventories of soil nitrogen, assessments of soil nutrient status, and management of arable land. [ABSTRACT FROM AUTHOR]

Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields. [ABSTRACT FROM AUTHOR]

Precipitation on the Qinghai–Tibet Plateau (TP) in southwestern China is subject to interactions between the complex and variable terrain and the sensitive climate. The regional climate is mainly affected by three circulations: westerlies, the South Asian monsoon, and the East Asian monsoon. Spatial and temporal variations in the rainy season onset were characterised based on daily precipitation from 106 meteorological stations on the TP from 1971 to 2015. Using the Theil–Sen Median trend analysis, Mann–Kendall test and mutation detection, the characteristics and reasons for the variations during the rainy season over the plateau over the past 45 years were investigated. The following results were obtained from the analysis: (1) There were obvious regional differences in the rainy season onset over the TP, and the rainy season began on the southeastern plateau and moved northwestward. (2) The TP rainy season underwent a significant mutation in approximately 1997, and following this mutation, the area affected by the delayed rainy season increased. (3) Against the background of global warming, the rainy season trend over the TP was advanced; however, there were still several multiple contiguous concentrated areas on the plateau. (4) Before the rainy season mutation, there were two centres of delayed precipitation on the plateau, which existed primarily due to their location at the end of the plateau water vapour transport channel. After the mutation, the number of delayed precipitation centres on the plateau increased to three and presented a spatially expanding trend, which may be related to the weakening trend in atmospheric circulation. [ABSTRACT FROM AUTHOR]

The worldwide development of multi-center structures in large cities is a prevailing development trend. In recent years, China's large cities developed from a predominantly mono-centric to a multi-center urban space structure. However, the definition and identification city centers is complex. Both nighttime light data and point of interest (POI) data are important data sources for urban spatial structure research, but there are few integrated applications for these two kinds of data. In this study, visible infrared imaging radiometer suite (NPP-VIIRS) nighttime imagery and POI data were combined to identify the city centers in Hangzhou, China. First, the optimal parameters of multi-resolution segmentation were determined by experiments. The POI density was then calculated with the segmentation results as the statistical unit. High–high clustering units were then defined as the main centers by calculating the Anselin Local Moran's I, and a geographically weighted regression model was used to identify the subcenters according to the square root of the POI density and the distances between the units and the city center. Finally, a comparison experiment was conducted between the proposed method and the relative cut-off_threshold method, and the experiment results were compared with the evaluation report of the master plan. The results showed that the optimal segmentation parameters combination was 0.1 shape and 0.5 compactness factors. Two main city centers and ten subcenters were detected. Comparison with the evaluation report of the master plan indicated that the combination of nighttime light data and POI data could identify the urban centers accurately. Combined with the characteristics of the two kinds of data, the spatial structure of the city could be characterized properly. This study provided a new perspective for the study of the spatial structure of polycentric cities. [ABSTRACT FROM AUTHOR]

[Display omitted] • A finest map (30 m) of soil pH in cropland of Southern China was produced. • The performance of different variable selection methods was compared. • The performance of different spatial predictive algorithms on mapping soil pH was evaluated. • We analyzed the application of our produced pH map on soil heavy metal pollution. • The best applicable scenarios of RFE and FRFS were validated and analyzed. Soil pH is one of the critical indicators of soil quality. A fine resolution map of soil pH is urgently required to address practical issues of agricultural production, environmental protection, and ecosystem functioning, which often fall short of meeting the demands for local applications. To fill this gap, we used data from an extensive survey of 13,424 surface soil samples (0–0.2 m) across the cropland of Jiangxi Province in Southern China. Using digital soil mapping techniques with 46 covariates, we produced a 30 m resolution soil map of topsoil pH in cropland of Southern China. We integrate different variable selection algorithms and machine learning methods. Our results indicate the Random Forest with covariates selected by recursive feature selection had the best performance for mapping soil pH with r of 0.583 and RMSE of 0.41. The prediction interval coverage probability for our prediction was 0.92, indicating a low estimated prediction uncertainty. Climate was identified as the most critical variable for predicting soil pH with a contribution of 37.42 %, followed by soil properties (29.09 %), soil management (21.86 %), parent material (6.22 %), and biota (5.39 %) factors. The mean topsoil pH in the cropland of Jiangxi Province was 5.21, indicating a great pressure of soil acidification in this region. The high soil pH values were mainly distributed in the Northern, Western, and Eastern parts of the survey region while low soil pH values were majorly located in the central part. Compared with past surveys in 1980 s, there was no significant soil pH change in the surveyed region. Our soil pH map can provide important implications and guidance decisions on soil heavy metal pollution remediation, precision agriculture, and prevention of soil acidification. [ABSTRACT FROM AUTHOR]

Mapping the spatiotemporal distribution of soil heavy metals is a prerequisite for soil pollution prevention and control. However, current mapping methods make it difficult to balance robustness and interpretability. In this study, we proposed a spatiotemporal interpretable mapping framework for soil heavy metals and conducted a case study of soil cadmium (Cd) in cropland of the Poyang Lake region in China. The results showed that the average soil Cd concentration increased significantly (p < 0.01) from 0.13 mg/kg in 1983 to 0.19 mg/kg in 2010. Soil Cd in 1983 was mainly controlled by human activities and topography, whereas it was jointly driven by human activities, topography, and climate in 2010. In addition, the framework revealed the spatial distribution of soil Cd and their uncertainties at a 12.5-m resolution in 1983 (R 2 = 0.94) and 2010 (R 2 = 0.97). The top three covariates in 1983 were nighttime-light, PM 10 , and land surface temperature (day), whereas those in 2010 were mean annual minimum temperature, gross domestic product, and industrial enterprises, with Shapley additive explanation (SHAP) values of 0.44, 0.42, and 0.27, and 0.31, 0.11, and 0.09 mg/kg, respectively. By integrating machine learning, variable and model selection, and the SHAP module, the proposed framework achieves a balance between the robustness and interpretability in mapping the spatiotemporal distribution of soil Cd, going beyond the limitations of the classical statistical "smoothing effect" and the artificial intelligence "black box." Our study emphasizes the importance of considering time, space, models, variables, and mechanisms in digital soil mapping, which will enable more accurate and scientific predictions of soil contaminants across time and space. [Display omitted] • Developed a spatiotemporal interpretable mapping framework of soil heavy metal at a 12.5-m resolution. • Quantified the driving mechanism and spatiotemporal clustering pattern of soil Cd. • Integrated advanced digital soil mapping technologies and the explainable SHAP module. • Achieved a balance between robustness and interpretability. [ABSTRACT FROM AUTHOR]

[Display omitted] Polycyclic aromatic hydrocarbons (PAHs) and carbon dioxide primarily originate from the combustion of fossil fuels and biomass. The implementation of the Chinese "double carbon strategy" is expected to impact the distribution of PAH emissions, consequently influencing the spatial distribution trend of PAHs in surface soil. Therefore, it is crucial to quantitatively evaluate the effectiveness of the Chinese "double carbon strategy" on soil PAH pollution for the purpose of "the reduction of pollution and carbon emissions". This study utilized 15,088 individual PAH concentration data from 943 soil samples collected between 2003 and 2020 in China, in conjunction with PAH emissions at a 10 km resolution, for meta -analysis. The calculated PAH emissions in this study are in line with the global PAH emission inventory (PKU-PAH-2007), with a relative standard deviation at the provincial level of less than 25 %. Subsequently, a novel method was developed using emission density and K ow of PAHs to predict PAH concentrations in surface soil based on a least-squares regression model. Compared to other environmental models, the method established in this study significantly reduced the percent sample deviation to less than 70 %. Furthermore, energy consumption data for China were simulated based on the implementation plan of the "double carbon strategy" to project PAH emissions and soil PAH levels for the years 2030 and 2060. The predicted PAH emissions in China were estimated to decrease to 41,300 t in 2030 and 10,406.5 t in 2060 from 78,815 t in 2020. Moreover, the heavily contaminated areas of soil PAHs (i.e., total PAH concentrations in soil exceeding 1000 μg kg−1) were projected to decrease by 45 % and 82 % in 2030 and 2060, respectively, compared to levels in 2020. These findings suggest that the implementation of the "double carbon strategy" can fundamentally reduce the pollution of PAHs in surface soil of China. [ABSTRACT FROM AUTHOR]

[Display omitted] The estimation of long-term fine particulate matter (PM 2.5) concentrations and trend assessments play a critical role in preventing health risks to the human body. Deep learning methods based on time series have been highly accurate in predicting PM 2.5. However, most time series models lack the ability for spatial generalization because they cannot combine the analysis at different spatial scales. In addition, temporal long-term trend analysis have not been reported in most of the studies. In this study, to reveal the spatiotemporal variability and trends of PM 2.5 , an improved deep learning framework named the SpatioTemporal Enhanced Neural Network (STENN) is developed for estimating PM 2.5 concentrations with a spatial resolution of 1 km. Based on the bidirectional long short-term memory (LSTM) structure and attention mechanisms, the model provides a geographic-data-driven approach to incorporate the impact of the spatial heterogeneity and time dependence of PM 2.5 , which demonstrates that it has robust spatiotemporal transferable power with an R2 of 0.89 produced by cross validation (CV). High-resolution (1 km) and high-quality annual PM 2.5 products for mainland China from 2014 to 2020 were constructed. In comparison with the current 1-km PM 2.5 products, our framework demonstrates better stability in different regions, especially in terms of the high-value estimations and spatial continuity. The spatiotemporal PM 2.5 distributions were also analyzed based on the time-series products. After the implementation of various control policies for atmospheric pollution, a declining trend of PM 2.5 concentrations was observed in 88.79% of China between 2014 and 2020, with a mean decrease rate of 3.35 μg m−3 yr−1. This result indicates that the control policies of the Chinese government were effective in reducing PM 2.5 concentrations. The PM 2.5 concentrations in China reveal an exponential temporal trend, from a rapid decline to a gradual slowdown and a stable phase. To realize the vision of the Beautiful China Initiative, a regionally targeted policy for air pollution management is required. This study provides valuable implications for a more detailed analysis of the spatiotemporal variations in PM 2.5 at small and medium spatial scales by developing an improved deep learning approach with a spatial generalization ability and integration with multi-temporal satellite products. [ABSTRACT FROM AUTHOR]

[Display omitted] Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development. [ABSTRACT FROM AUTHOR]

Although numerous studies have reported the influencing factors of polycyclic aromatic hydrocarbons (PAHs) in surface soil from source, process or soil perspectives, the mechanism of PAHs heterogeneity in surface soil are still not well understood. In this study, the effects of 16 PAHs in surface soil of China sampled between 2003 and 2020 with their 17 "source-process-sink" factors at 1 km resolution (N = 660)) were explored using deep learning (eXtreme Gradient Boosting) to mine key information from complex dataset under the optimized parameters (i.e., learning rate = 0.05, maximum depth = 5, sub-sample = 0.8). It was observed that top five factors of 16 PAH had the largest cumulative contribution (i.e., from 84.8% to 98.1%) on their soil concentrations. PAH emission was the predominant driver, and its effect on soil PAH increases with increasing log K ow. Soil was the second driver, in which clay can promote the partition of PAHs with low or middle log K ow. However, sand can accumulate those congeners with high log K ow. Moreover, the deep learning plus geo-statistical models (with low deviation for testing dataset (N = 283)) were capable of predicting soil PAH concentrations using their drivers with high accuracy. This study improved the understanding of the environmental fate and spatial variability of soil PAHs, as well as provided a novel technique (i.e., deep learning coupled with geo-statistics) for accurate prediction of soil pollutants. [Display omitted] • Contribution of "source-process-sink" factors to soil PAHs were quantified. • Five key drivers of each soil PAH were distinguished. • Contributions of drivers to soil PAHs were ranged from 84.8% to 98.1%. • More attention should be paid to PAHs pollution in soil along "Hu line". • A hybrid model was proposed to accurately model surface soil PAHs. [ABSTRACT FROM AUTHOR]

[Display omitted] • We produced a high resolution (30 m) SOC map in cropland of Southern China. • Our produced SOC map has high accuracy with R2 = 0.73 and RMSE = 2.91 g kg−1. • The map accuracy was enhanced by incorporating soil management information. • Soil properties and soil management measures are most relevant to SOC variation in cropland. An accurate and fine map of soil organic carbon (SOC) plays a vital role in understanding the global carbon cycle and achieving soil carbon sequestration potential. Although global and national maps of SOC are already available with various spatial resolutions, limited sample size, and relative coarse resolution hinder its accuracy and application in local regions. Here, we collected 13,424 soil samples and information of 45 environmental covariates from cropland in Jiangxi Province, Southern China during 2012 and 2013. Then, the optimal covariates were selected by a recursive feature elimination algorithm for mapping SOC. After that, we deployed the random forest (RF) to produce a fine map (30 m) of SOC in the cropland of Jiangxi Province and 100 times bootstrapping was performed to calculate the prediction uncertainty. Finally, we determined the impacts of various covariates on SOC variability using RF and partial least squares structural equation modeling. Our results showed that compared with the predictive model without soil management information, introducing soil management information improved the predictability of SOC with an increase in R2 by 7.35 % (0.73 vs 0.68) and decrease in RMSE by 7.03 % (2.91 vs 3.13 g kg−1). Our results well estimated the uncertainty of the predicted result with a PICP of 0.91 for a 90 % prediction interval. Soil properties and soil management activities make the largest contribution for modelling SOC. Specifically, the total nitrogen content, straw return amount, total potassium content, and multi-resolution valley bottom flatness were found as the most important factors for mapping SOC in the cropland of our study area. Overall, this study deepened our knowledge of the variation of SOC and also emphasizes that incorporating soil management information could help us to achieve more accurate predictions of SOC. [ABSTRACT FROM AUTHOR]

• Deformation of 18 sections in Grade V rock mass and the initial support structural force of 2 sections are analyzed. • FLAC 3D numerical simulation is used to study the deformation of the rock mass. • The supporting parameters of fifth-grade surrounding rock for large-span loess highway tunnel are designed. When a large span loess tunnel is excavated, the rock mass is disturbed, and it is difficult to form a load-bearing arch under shallow depth region. As a result, the structural deformation is complicated. This paper analyzed the deformation of 18 sections in Grade V rock mass and the primary support structural force of 2 sections in a case study of a highway loess tunnel in the large span loess highway tunnel group of Huangyan Traffic Capacity Expansion Engineering in Northern Shaanxi Province-Jianzicha No.2 Tunnel. The analyzed results were combined with the FLAC 3D numerical simulation to study the deformation of rock mass in Jianzicha No.2 tunnel and the stress characteristics of primary support. The results showed that: (1) The time-space relationship curve of arch deformation indicated three stages of "rapid deformation stage -continuous deformation stage-slow deformation stage" in the construction by annular excavation with reserved core soil method. (2) The rock mass pressure distribution of tunnel basically presented the bilateral symmetry characteristics. The rock mass pressure distribution of arch was relatively uniform, and the rock mass pressure at the invert was relatively large. (3) The outer and inner sides of primary support arch steel rib was subjected to compression, and the stress distribution was relatively uniform and large. (4) The primary support shotcrete was mainly subjected to compression, and the stress distribution was basically characterized by bilateral symmetry. The stress distribution of shotcrete at the arch was relatively uniform and large, and the stress of shotcrete at the inverted part was relatively small. This paper studied the time-space law of supporting structure deformation in the shallow section of Ⅴ grade surrounding rock, and designs the supporting parameters of Ⅴ grade surrounding rock for large-span loess highway tunnel, which has guiding significance for the construction of similar projects in the future. [ABSTRACT FROM AUTHOR]

• N-MBL was compared with Machine Learning and local models. • N-MBL explained the non-linear relationship well between the property and spectra. • The performance of N-MBL was better than Machine Learning models. • The number of selected nearest neighbors was related to the accuracy of local model. Visible near-infrared (vis-NIR) spectroscopy has gained widespread recognition as an efficient and reliable approach for the rapid monitoring of soil properties. This technique relies on robust machine learning models that convert soil spectra information to soil properties. In particular, memory-based learning (MBL) has emerged as a powerful local modeling technique for soil spectral analysis. However, conventional MBL algorithms use linear models, disregarding the non-linear relationship between soil properties and vis-NIR spectra. Therefore, we hypothesize that non-linear memory-based learning (N-MBL) models can enhance prediction. This study develops and evaluates the N-MBL algorithm using the Lateritic Red soil spectral library (LRSSL) from Guangdong province in China. This library consists of 742 samples of vis-NIR spectra and corresponding soil properties, including pH, soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and total potassium (TK). As a comparison, several commonly used supervised learning methods, such as Partial least squares regression (PLSR), Cubist, Random Forest (RF), Super vector machine (SVM), Convolution neural network (CNN), and local models (MBL), were compared to the proposed N-MBL. The results showed that local models generally outperformed supervised learning methods, particularly when applied to a large soil spectral library with a substantial number of samples (over 500). When comparing the two local models, MBL had more fluctuation of model performance compared to N-MBL as the number of selected nearest neighbors (k) varied between 30 and 250. As k increased, N-MBL showed higher R2 values for SOM and TN prediction than MBL but lower performance for pH and TK prediction. In addition, N-MBL outperformed MBL in predicting TP. In conclusion, N-MBL is a new local algorithm for predicting soil properties from vis-NIR spectra. It has a high potential to improve the accuracy of the prediction of multiple soil properties. [ABSTRACT FROM AUTHOR]

• We mapped the spatial processes of urban carbon metabolism by LUCC in cities. • The relationship between urban size growth and M LUCC is investigated. • The land use transfer largely affected urban carbon metabolism processes. • The growths of urban population and land both correlated positively with M LUCC. Rapid urbanization with land use and cover change (LUCC) is making a substantially increasing contribution to global carbon emissions. Understanding the spatial processes and transitions mechanism of urban carbon metabolism system by LUCC could help local governments in regional spatial planning. Taking 13 cities in the Yangtze River Delta of China as examples, we quantitatively analyzed and mapped the spatial processes of urban carbon metabolism by LUCC from 1995 to 2015 in the region and investigated the relationships between urban size growth and urban carbon metabolism rate by LUCC (M LUCC) using panel data regression analysis. A higher M LUCC showed a larger negative impact on the urban carbon metabolism system by per unit area of land use change. We found that the highest negative carbon transitions were shifted from Shanghai to cities in the South Jiangsu Province. And the dominant negative carbon transitions and positive ones came from land use transfer into and out the industrial land and transportation land. The results of the panel model regression analysis showed the growths of urban population and land both correlated positively with M LUCC. Further, we controlled the economic growth and urban form changes on the relationship between urban size growth and M LUCC , and the results suggested both the benefits from compromising economic growth and optimizing urban form were overshadowed by the negative impact of urban size growth. The study provided a robust methodology for assessing urban carbon metabolism and provided new insights into land use controls to develop low carbon cities. [ABSTRACT FROM AUTHOR]

Large-scale soil organic carbon (SOC) stock assessment is expensive as a large number of samples must be collected and then their time-consuming measurements must be made in the laboratory. Previous studies have shown that visible-near-infrared reflectance (vis-NIR) spectroscopy can quickly predict SOC content at a low cost. However, the application of this method at the large scale remains challenging due to the high spatial heterogeneity of SOC and the spatially dependent relationships of soil spectra and SOC content. Here, we conducted large-scale soil sampling across China's forests and established the Chinese forest soil spectral library (CFSSL) by measuring SOC content and scanning the vis-NIR reflectance of 11, 213 soil samples. Compared with the traditional global partial least squares regression (PLSR) modeling method (R 2 = 0.75, RPIQ = 1.95), the clustering by fast research and find of density peak in combination with the Cubist model significantly improved the prediction ability of SOC content (R 2 = 0.96, RPIQ = 5.83). This study provided a cost-efficient spectroscopic methodology, including measurement and prediction modeling, for large-scale SOC estimation. • Chinese forest soil vis-NIR spectral library was developed with 11, 213 soil samples. • PLSR model is not able to precisely predict SOC content at large-scale. • DP + Cubist model significantly improves the spectroscopic prediction of SOC. [ABSTRACT FROM AUTHOR]

Revealing the spatio-temporal heterogeneity of cropland ecosystem services and identifying its main driving factors is a prerequisite for managing cropland and ensuring sustainable agriculture. The cropland ecosystem services are impacted by both cultivation practices and the natural environment, which lead to high heterogeneity. Therefore, making clear the cropland ecosystem service value (CESV) dynamic and its driving factors are still a great challenge in agricultural production areas. Jiangxi Province is located in eastern China and it is one of the main grain production regions in China but face great pressure of cropland degradation due to rapid industrialization and urbanization in the past two decades. However, the knowledge on the spatio-temporal variation of CESV and related dominators is largely retain unclear in Jiangxi Province. To fill this gap, we integrated different biophysical models and value estimation methods to map the CESV in Jiangxi Province. The CESV from 2001 to 2020 was mapped with a spatial resolution of 1 km, and the temporal trend was explored using Theil-Sen and Mann-Kendall test method. Subsequently, the influencing pathways of both natural and anthropogenic driving factors on CESV were contrasted utilizing partial least squares structural equation modeling. The spatial correlation and heterogeneity between CESV and its driving factors was also revealed by the bivariate Moran's I method. Finally, the geographically weighted regression was adopted to identify spatially-varying primary driving factors which dominate the variation of CESV across the study area. Our results revealed that: (1) the CESV in Jiangxi province increased from 257.84 billion yuan to 312.37 billion yuan from 2001 to 2020; (2) the CESV in most of the study area (97.47%) was featured by an increasing trend; (3) vegetation and terrain factors have the highest contribution to the variation of CESV, followed by soil properties; (4) the CESV was positively correlated with saturated soil hydraulic conductivity, clay, elevation, slope, leaf area index, vegetation cover fraction in the study area, while significantly negatively correlated with population density and nighttime light index; (5) the proportion of areas where CESV was dominated by vegetation cover fraction was the largest, followed by saturated soil hydraulic conductivity, elevation, and slope. Based on our findings, we put forward several suggestions to underpin sustainable cropland ecosystem management and development. Our research offers an insightful understanding of spatio-temporal heterogeneity of CESV and can provide important implications and useful guidance for enhancing the ecosystem functions of cropland. [Display omitted] • The total CESV of Jiangxi Province increased from 257.84 billion yuan in 2001 to 312.37 billion yuan in 2020. • The CESV across Jiangxi Province was mapped from 2001 to 2020 with a spatial resolution of 1 km. • The temporal trend of CESV was explored using Theil-Sen and Mann-Kendall test. • The CESV in Jiangxi Province was promoted directly by terrain and vegetation. • Spatial varying primary drivers of spatio-temporal variation of CESV were mapped. [ABSTRACT FROM AUTHOR]

[Display omitted] • All four deep learning models can perform excellent potential for estimating SIC. • LSTM was the best-performing deep learning algorithm for estimating SIC. • The variable selection algorithm could improve the estimation of SIC. • The IRF was the best algorithm to select spectral variables relevant to SIC. Soil inorganic carbon (SIC) is the primary component of the soil carbon pool in arid and semiarid regions and strongly impacts the global carbon cycle, ecosystem services, and soil functions. The global climate change and intensify of human activities, could substantially change SIC, which highlights the importance of monitoring SIC. Rapid and accurate estimation of SIC concentration is critical for soil inorganic carbon pool monitoring. Currently, visible near-infrared (Vis-NIR) spectroscopy is a promising technique for estimating SIC via a rapid and cost-effective manner. Thus, in this study, we collected 315 topsoil samples from the Alar Reclamation Area in South Xinjiang, China, and measured their Vis-NIR spectra and SIC content. Then, we used deep learning algorithms, including a one-dimensional convolutional neural network (1D-CNN), two-dimensional convolutional neural network (2D-CNN), long short-term memory network (LSTM), and deep belief network (DBN), combined with variable selection algorithms (particle swarm algorithm (PSO), interval random frog (IRF), competitive adaptive reweighting algorithm (CARS), ant colony algorithm (ACO), and iteratively retaining informative variables (IRIV) to estimate SIC. Results showed that all five variable selection algorithms could effectively extract the featured spectral information of SIC, and reduce the number of spectral variables by >97%, simplifying the model structure. The variable selection algorithm could markedly improve the SIC spectral estimation accuracy, and the corresponding estimation accuracy follows the order: IRF > IRIV > PSO > CARS > ACO. All four deep learning models have high prediction accuracy, and the modeling accuracy of each method follow the order: LSTM > 1D-CNN > 2D-CNN > DBN. The combined IRF and LSTM model achieved the highest estimation accuracy (R2 = 0.93, RMSE = 1.26 g kg−1 in the calibration dataset; R2 = 0.92, RMSE = 1.37 g kg−1 in the validation dataset). This study demonstrated that deep learning combined with variable selection algorithms can detect SIC content quickly and accurately. [ABSTRACT FROM AUTHOR]

• Laboratory vis–NIR and MIR spectra were compared and fused to predict SOC. • A parallel-input convolutional neural network data fusion framework was presented. • Proposed method outperformed commonly-used data fusion strategies for SOC. • The combinational use of vis–NIR and MIR could improve the prediction for SOC. Visible-to-near-infrared (vis–NIR) and mid-infrared (MIR) spectroscopy have been widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis–NIR and MIR data can be hypothesized to provide accurate and reliable prediction for SOC because spectral data within a specific range of each individual sensor may lack important absorptive features associated with SOC. In this study, six data fusion strategies, principally direct concatenation-partial least squares regression (DC-PLSR), outer product analysis-PLSR (OPA-PLSR), OPA-competitive adaptive reweighted sampling-PLSR (OPA-CARS-PLSR), sequentially orthogonalized-PLSR (SO-PLSR), DC-convolutional neural network (DC-CNN), and parallel input-CNN (PI-CNN), were compared for the spectral estimations of SOC. The data fusion and individual sensor models were developed using soil samples collected from Zhejiang Province, East China, and scanned under laboratory conditions with both vis–NIR and MIR spectrophotometers. The validation results of vis–NIR (validation coefficient of determination [ R 2] = 0.63–0.73) were generally better than those of MIR (validation R 2 = 0.45–0.59). For data fusion, the best validation accuracy was achieved by the PI-CNN (validation R 2 = 0.84), followed in descending order by DC-CNN (validation R 2 = 0.78), SO-PLSR (validation R 2 = 0.73), OPA-CARS-PLSR (validation R 2 = 0.69), OPA-PLSR (validation R 2 = 0.66), and DC-PLSR (validation R 2 = 0.64). The better performance of PI-CNN over DC-CNN demonstrates the necessity of using different sizes of convolutional kernels before feeding into the fully connected layers in the CNN network for fusing vis–NIR and MIR spectral data. The deep-learning fusion method based on PI-CNN can be considered an efficient tool for integrating data from multiple sensors for estimating soil properties in the field of soil spectral modeling. [ABSTRACT FROM AUTHOR]

Surface ozone (O 3), a well-recognized air pollutant, exists in the atmosphere, which has a detrimental effect on public health and the ecological environment. It is reported that surface O 3 has seen a significant increase in many cities from 2019 to 2021 (COVID-19 pandemic). In this study, we applied an innovative machine learning model (Deep Forest) coupled with satellites, the Troposphere Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI), and meteorological datasets to estimate monthly surface O 3 of 1 km spatial resolution across China during this pandemic period. Our model achieved an overall R2 of 0.974, 0.963, and root mean square error (RMSE) of 6.016 μg/m3, 7.214 μg/m3 on TROPOMI-based datasets and OMI-based datasets, respectively. Also, we found the higher ozone concentration regions were in Eastern China. Simultaneously, the surface O 3 concentration was high in summer(average = 110.57 ± 15.01 μg/m3). And the ozone concentration in summer 2020 (average = 107.78 ± 13.90 μg/m3) declined unprecedently than in summer 2019 (average = 110.54 ± 16.58 μg/m3). Our results indicated that TROPOMI data could provide robust data support for surface ozone concentration estimation. Furthermore, this study could enhance our comprehension of the formation mechanisms of surface O 3 in China and assist air environment management decision-making. • Deep Forest model is applied to predict the surface ozone concentration in China. • TROPOMI can mitigate the spatial resolution deficiency of OMI satellite data in surface ozone concentration estimation. • High spatial resolution (1 km) monthly surface ozone concentration products from 2019 to 2021 are generated. • Our results provide a theoretical supplementary for air environment management. [ABSTRACT FROM AUTHOR]

• Both DHI Cum and DHI Min had strong explanatory power for estimating species richness. • The species habitat appropriateness in the areas with high species habitat appropriateness tended to decrease over time in China. • The species habitat appropriateness in the areas with low species habitat appropriateness tended to increase over time in in China. • The correlation factors for the variation of biodiversity varied spatially in China. For development planning and biodiversity management in China, it is crucial to understand the past and present patterns of biodiversity. Using satellite data has proven to be an effective means of characterizing the spatial distribution of species based on the species energy hypothesis, thereby supporting the conservation of biodiversity. This study's specific purposes included assessing different surrogates for annual species richness in China using MODIS as input for the Dynamic Habitat Indices (DHIs) and analyzing the trend and triggers of variation in DHI from 2003 to 2018. We evaluated the linear relationships between the species richness and different DHIs (containing DHI Cum, cumulative productivity; DHI Min, minimum productivity; DHI Var, intra-annual variation of productivity) in China. Further, we conducted a least squares linear regression analysis to investigate the long-term trend of the best performed DHI, as well as an analysis of correlation factors. Our results demonstrated that both DHI Cum and DHI Min had strong explanatory power for estimating species richness, while DHI Var had a poor performance. Among all individual DHIs, GPP-DHI Cum performed the best. The trend analysis showed that the species habitat appropriateness in the areas with high species habitat appropriateness tended to decrease over time, while that in the areas with low species habitat appropriateness tended to increase over time. We also found that the correlation factors for the variation of biodiversity varied spatially. The decrease of biodiversity in the south and eastern coast of China was associated with the decrease of the solar radiation, the increases of the temperature and precipitation, and the expansion of human footprint. By comparison, the increase of biodiversity in the Tibetan Plateau was associated with the increases of the temperature while that in the Loess Plateau was associated with the increases of the precipitation. In addition to climatic factors, the implementations of ecological restoration projects have also driven increased diversity. Besides an improved understanding of biodiversity dynamics, these findings may help to promote biodiversity conservation and policy making. [ABSTRACT FROM AUTHOR]

Potentially toxic elements in soils (SPTEs) from industrial and mining sites (IMSs) often cause public health issues. However, previous studies have either focused on SPTEs in agricultural or urban areas, or in a single or few IMSs. A systematic assessment of the pollution and risk levels of SPTEs from IMS at the national scale is lacking. Here, we obtained SPTE (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) concentrations from IMSs across China based on 188 peer-reviewed articles published between 2004 and 2022 and quantified their pollution and risk levels using the pollution index and risk assessment model, respectively. The results indicated that the average concentrations of the eight SPTEs were 4.42–270.50 times the corresponding background values, and 19.58% of As, 14.39% of Zn, 12.79% of Pb, and 8.03% of Cd exceeded the corresponding soil risk screening values in these IMSs. In addition, 27.13% of the examined IMS had one or more SPTE pollution, mainly distributed in the southwest and south central China. On the examined IMSs, 81.91% had moderate or severe ecological risks, which were mainly caused by Cd, Hg, As, and Pb; 23.40% showed non-carcinogenic risk and 11.70% demonstrated carcinogenic risk. The primary exposure pathways of the former were ingestion and inhalation, while that for the latter was ingestion. A Monte Carlo simulation also confirmed the health risk assessment results. As, Cd, Hg, and Pb were identified as priority control SPTEs, and Hunan, Guangxi, Guangdong, Yunnan, and Guizhou were selected as the key control provinces. Our results provide valuable information for public health and soil environment management in China. [Display omitted] • Industrial and mining sites were more polluted than urban and agricultural areas. • 27.13% of the examined industrial and mining sites reached pollution levels. • 11.70% of the examined industrial and mining sites demonstrated carcinogenic risks. • Identified the key areas and pollution elements on a national scale. [ABSTRACT FROM AUTHOR]