The targeting of DAPK1 with miR-190a-3p promotes autophagy in trophoblast cells.

Pre-eclampsia (PE) is a dangerous pathological status that occurs during pregnancy and is a leading reason for both maternal and fetal death. Autophagy is necessary for cellular survival in the face of environmental stress as well as cellular homeostasis and energy management. Aberrant microRNA (miRNA) expression is crucial in the pathophysiology of PE. Although studies have shown that miRNA (miR)-190a-3p function is tissue-specific, the precise involvement of miR-190a-3p in PE has yet to be determined. We discovered that miR-190a-3p was significantly lower and death-associated protein kinase 1 (DAPK1) was significantly higher in PE placental tissues compared to normal tissues, which is consistent with the results in cells. The luciferase analyses demonstrated the target-regulatory relationship between miR-190a-3p and DAPK1. The inhibitory effect of miR-190a-3p on autophagy was reversed by co-transfection of si-DAPK1 and miR-190a-3p inhibitors. Thus, our data indicate that the hypoxia-dependent miR-190a-3p/DAPK1 regulatory pathway is implicated in the development and progression of PE by promoting autophagy in trophoblast cells.

Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China.

Soil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data to monitor soil salinization, 172 Landsat 8 images from 2013 to 2019 were obtained from the Alar Reclamation Area of Xinjiang, northwest China. The multiyear extreme dataset was synthesized from the annual maximum or minimum values of 16 vegetation indices, which were combined with the soil conductivity of 540 samples from soil profiles at 0~0.375 m, 0~0.75 m and 0~1.00 m depths in 30 cotton fields with varying degrees of salinization as investigated by EM38-MK2. Three remote sensing monitoring models for soil conductivity at different depths were constructed using the Cubist method, and digital mapping was carried out. The results showed that the Cubist model of soil profile electrical conductivity from 0 to 0.375 m, 0 to 0.75 m and 0 to 1.00 m showed high prediction accuracy, and the determination coefficients of the prediction set were 0.80, 0.74 and 0.72, respectively. Therefore, it is feasible to use a multiyear extreme value for the vegetation index combined with a Cubist modeling method to monitor soil profile salinization at a regional scale.

A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information.

Remote sensing is an important tool for monitoring soil information. However, accurate spatial modeling of soil organic matter (SOM) in areas with high vegetation coverage, typically represented by agroecosystems, remains a challenge for field-scale estimation using remote sensing. To date, studies have focused on using single-period or multi-temporal vegetation information to characterize SOM. Thus, the relationship between SOM content and time-series vegetation biomass has not yet been fully explored. In addition, most studies have ignored the effects of critical soil properties and human activities (e.g., soil salinization, soil particle size fractions, history of land-use changes) on SOM. By integrating information on vegetation, soil, and human activities, we propose a novel framework for assessing SOM in cotton fields of artificial oases in northwest China, where returned straw is one of the primary sources of SOM coming from vegetation. We developed an Annual Maximum Biomass Accumulation Index (AMBAI) using time-series Landsat images from 1990 to 2019. Subsequently, we quantified the information of the planting years (PY) of cropland using spectral index threshold and incorporated proximal sensing data (soil hyperspectral and apparent conductivity data) and soil particle size fractions to establish a predictive model of SOM using partial least squares regression (PLSR), random forest (RF), and convolutional neural network (CNN). The results revealed that AMBAI had the highest correlation coefficient (r) with SOM (0.76, P < 0.01). AMBAI, soil hyperspectral data, and PY were the most relevant predictors for estimating SOM. The CNN model integrating vegetation, soil, and human activity information performed best, with coefficient of determination (R2), relative analysis error (RPD), and root mean square error (RMSE) of 0.83, 2.38 and 1.38 g kg-1, respectively. This study confirmed that AMBAI and PY had great potential for characterizing SOM in arid and semi-arid regions, providing a reference for other relevant studies.

Comparison of Depth-Specific Prediction of Soil Properties: MIR vs. Vis-NIR Spectroscopy.

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500-25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0-100 cm) or the shallow layers (0-40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350-2500 nm). A total of 90 soil samples containing 450 subsamples (0-10 cm, 10-20 cm, 20-40 cm, 40-70 cm, and 70-100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07-3.82 g/kg, RPD = 1.10-5.80; TN: RMSEp = 0.11-0.15 g/kg, RPD = 1.70-4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75-8.95 g/kg, RPD = 0.50-3.61; TN: RMSEp = 0.12-0.27 g/kg; RPD = 1.00-3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09-2.97 g/kg, RPD = 2.13-5.80; TN: RMSEp = 0.08-0.19 g/kg, RPD = 1.86-4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07-8.95 g/kg, RPD = 0.50-3.93; TN: RMSEp = 0.11-0.27 g/kg, RPD = 1.00-2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.

Pollution and risk assessment of potentially toxic elements in soils from industrial and mining sites across China.

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.

How the Air Clean Plan and carbon mitigation measures co-benefited China in PM2.5 reduction and health from 2014 to 2020.

China implemented a stringent Air Clean Plan (ACP) since 2013 to address environmental and health risks caused by ambient fine particulate matter (PM2.5). However, the policy effectiveness of ACP and co-benefits of carbon mitigation measures to environment and health are still largely unknown. Using satellite-based PM2.5 products produced in our previous study, concentration-response functions, and the logarithmic mean Divisia index (LMDI) method, we analyzed the spatiotemporal dynamics of premature deaths attributable to PM2.5 exposure, and quantitatively estimated the policy benefits of ACP and carbon mitigation measures. We found the annual PM2.5 concentrations in China decreased by 33.65 % (13.41 µg m-3) from 2014 to 2020, accompanied by a decrease in PM2.5-attributable premature deaths of 0.23 million (95 % confidence interval (CI): 0.22-0.27), indicating the huge benefits of China ACP for human health and environment. However, there were still 1.12 million (95 % CI: 0.79-1.56) premature deaths caused by the exposure of PM2.5 in mainland China in 2020. Among all ACP measures, clean production (contributed 55.98 % and 51.14 % to decrease in PM2.5 and premature deaths attributable to PM2.5) and energy consumption control (contributed 32.58 % and 29.54 % to decrease in PM2.5 and premature deaths attributable to PM2.5) made the largest contribution during the past seven years. Nevertheless, the environmental and health benefits of ACP are not fully synergistic in different regions, and the effectiveness of ACP measures reduced from 2018 to 2020. The co-effects of CO2 and PM2.5 has become one of the major drivers for PM2.5 and premature deaths reduction since 2018, confirming the clear environment and health co-benefits of carbon mitigation measures. Our study suggests, with the saturation of clean production and source control, more targeted region-specific strategies and synergistic air pollution-carbon mitigation measures are critical to achieving the WHO's Air Quality Guideline target and the UN's Sustainable Development Goal Target in China.

Estimation of Soil Organic Carbon Using Vis-NIR Spectral Data and Spectral Feature Bands Selection in Southern Xinjiang, China.

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.

Preliminary risk assessment of regional industrial enterprise sites based on big data.

An accurate and inexpensive preliminary risk assessment of industrial enterprise sites at a regional scale is critical for environmental management. In this study, we propose a novel framework for the preliminary risk assessment of industrial enterprise sites in the Yangtze River Delta, which is one of the fastest economic development and most prominent contaminated regions in China. Based on source-pathway-receptors, this framework integrated text and spatial analyses and machine learning, and its feasibility was validated with 8848 positive and negative samples with a calibration and validation set ratio of 8:2. The results indicated that the random forest performed well for risk assessment; and its accuracy, precision, recall, and F1 scores in the calibration set were all 1.0, and the four indicators for the validation set ranged from 0.97 to 0.98, which was better than that for the other models (e.g., logistic regression, support vector machine, and convolutional neural network). The preliminary risk ranking of industrial enterprise sites by the random forest showed that high risks (probabilities) were mainly distributed in Shanghai, southern Jiangsu, and northeastern Zhejiang from 2000 to 2015. The relative importance of the site industrial, production, and geographical features in the random forest was 69%, 22%, and 9%, respectively. Our study highlights that we could quickly and effectively establish a priority (or ranking) list of industrial enterprise sites that require further investigations, using the proposed framework, and identify potentially contaminated sites.

Association of DNA Methylation Patterns in 7 Novel Genes With Ischemic Stroke in the Northern Chinese Population.

Background: Ischemic stroke is a highly complex disorder. This study aims to identify novel methylation changes in ischemic stroke. Methods: We carried out an epigenome-wide study of ischemic stroke using an Infinium HumanMethylation 850K array (cases:controls = 4:4). 10 CpG sites in 8 candidate genes from gene ontology analytics top-ranked pathway were selected to validate 850K BeadChip results (cases:controls = 20:20). We further qualified the methylation level of promoter regions in 8 candidate genes (cases:controls = 188:188). Besides, we performed subgroup analysis, dose-response relationship and diagnostic prediction polygenic model of candidate genes. Results: In the discovery stage, we found 462 functional DNA methylation positions to be associated with ischemic stroke. Gene ontology analysis highlighted the "calcium-dependent cell-cell adhesion via plasma membrane cell adhesion molecules" item, including 8 candidate genes (CDH2/PCDHB10/PCDHB11/PCDHB14/PCDHB16/PCDHB3/PCDHB6/PCDHB9). In the replication stage, we identified 5 differentially methylated loci in 20 paired samples and 7 differentially methylated genes (CDH2/PCDHB10/PCDHB11/PCDHB14/PCDHB16/PCDHB3/PCDHB9) in 188 paired samples. Subgroup analysis showed that the methylation level of above 7 genes remained significantly different in the male subgroup, large-artery atherosclerosis subgroup and right hemisphere subgroup. The methylation level of each gene was grouped into quartiles, and Q4 groups of the 7 genes were associated with higher risk of ischemic stroke than Q1 groups (p < 0.05). Besides, the polygenic model showed high diagnostic specificity (0.8723), sensitivity (0.883), and accuracy (0.8777). Conclusion: Our results demonstrate that DNA methylation plays a crucial part in ischemic stroke. The methylation of these 7 genes may be potential diagnostic biomarker for ischemic stroke.

Comprehensive source identification and apportionment analysis of five heavy metals in soils in Wenzhou City, China.

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.

Potential driving forces and probabilistic health risks of heavy metal accumulation in the soils from an e-waste area, southeast China.

The integrated analysis of the distribution characteristics, health risks, and source identification of heavy metals is crucial for formulating prevention and control strategies for soil contamination. In this study, the area around an abandoned electronic waste dismantling center in China was selected as the research area. The probabilistic health risks caused by heavy metals were evaluated by the Monte Carlo simulation. Random forest, partial least squares regression, and generalized linear models were utilized to predict heavy metal distributions and identify the potential driving factors affecting heavy metal accumulation in soil. The relationships of spatial variation between the heavy metal contents and environmental variables were further visualized. The results revealed that cadmium (Cd) and copper (Cu) were the primary soil pollutants in the study area and caused high ecological risks. The probabilistic health risk assessment indicated that the non-carcinogenic and carcinogenic risks for all populations were acceptable. However, children are more susceptible to heavy metal soil contamination than adults. The sensitivity analyses indicated that the total contents of soil heavy metals and soil ingestion rate were the dominant factors affecting human health. The random forest model, with R2 values of 0.41, 0.65, 0.57, 0.71, and 0.58 for Cd, Cu, Ni, Zn, and Pb, respectively, predicted the heavy metal concentrations better than the other two models. The distance to the nearest industrial enterprise, industrial output, and agricultural chemical input were the main factors affecting Cd, Cu, Zn, and Pb accumulations in the soil, and soil pH and soil parent material were the primary factors influencing Ni accumulation in the soil. The visualization results of the geographically weighted regression model showed a significant relationship between soil heavy metal contents and industrial activity level. This study could be utilized as a reference for policymakers to formulate prevention and control strategies for heavy metal pollution in agricultural areas.

Soil erosion modelling: A bibliometric analysis.

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

An integrated assessment methodology for management of potentially contaminated sites based on public data.

Ranking assessment of potentially contaminated sites (PCS) provides a great quantity of information (namely the risk screening list) that is usually examined by environmental managers, and therefore reduces the cost of risk management in terms of site investigation. Here we propose an integrated assessment methodology to establish a risk screening list of PCS in China using the Choquet integral correlation coefficient (ICC), which takes the uncertainty and interaction of PCS attributes into explicit account. The proposed method globally considers the importance and ordered positions of PCS attributes while reflecting their overall ranking. The model evaluation and actual validation results demonstrate the success in PCS ranking by the proposed method, which is superior to other methods such as the intuitionistic fuzzy multiple attribute decision-making, the technique for order preference by similarity to an ideal solution, and the weighted average. The resulting spatial distribution of Choquet ICC indicates that high-attention PCS in China are mainly located in Guangdong, Jiangsu, Zhejiang, and Shandong Provinces. This study is the first attempt to conduct a ranking assessment of PCS across China. The proposed assessment method based on Choquet ICC offers a step towards establishing a risk screening list of PCS globally.

Soil erosion modelling: A global review and statistical analysis.

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

Current Status and Temporal Trend of Potentially Toxic Elements Pollution in Agricultural Soil in the Yangtze River Delta Region: A Meta-Analysis.

Potentially toxic elements (PTEs) pollution in the agricultural soil of China, especially in developed regions such as the Yangtze River Delta (YRD) in eastern China, has received increasing attention. However, there are few studies on the long-term assessment of soil pollution by PTEs over large regions. Therefore, in this study, a meta-analysis was conducted to evaluate the current state and temporal trend of PTEs pollution in the agricultural land of the Yangtze River Delta. Based on a review of 118 studies published between 1993 and 2020, the average concentrations of Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni were found to be 0.25 mg kg-1, 0.14 mg kg-1, 8.14 mg kg-1, 32.32 mg kg-1, 68.84 mg kg-1, 32.58 mg kg-1, 92.35 mg kg-1, and 29.30 mg kg-1, respectively. Among these elements, only Cd and Hg showed significant accumulation compared with their background values. The eastern Yangtze River Delta showed a relatively high ecological risk due to intensive industrial activities. The contents of Cd, Pb, and Zn in soil showed an increasing trend from 1993 to 2000 and then showed a decreasing trend. The results obtained from this study will provide guidance for the prevention and control of soil pollution in the Yangtze River Delta.

Assessment of potentially toxic element pollution in soils and related health risks in 271 cities across China.

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.

Assessment of Heavy Metal Pollution in Soil and Classification of Pollution Risk Management and Control Zones in the Industrial Developed City.

Soil heavy metal pollution threatens ecological health and food security. It is significant to classify pollution risk management and control zones, which can effectively cope with soil pollution and scientifically carry out soil remediation projects. In this study, based on 665 soil samples collecting from Ningbo (southeast China), single pollution index and Nemerow composite pollution index (NCPI) were measured to assess soil pollution risk, and self-organization mapping model was applied to classify management and control zones. Results showed that the heavy metal pollution in the northwest part was more serious, while the east part was less polluted. Although more than 75% soil samples had negligible risks, the Hg and Cu pollution was greatly influential and notable as their polluted samples accounted for 24.21% and 12.48% respectively. Moreover, about 55.34% soil samples and more than half study region had pollution grades, and NCPI values were obviously high with the center of northwest study area. Results also showed that the study region could be classified into four zones with good spatial variabilities. Specifically, Monitored Zone with High-risk Pollution had the highest NCPI caused by human activities, while Controlled Zone with Severe Pollution had relatively high NCPI caused by industrial and agricultural production. Protected Zone with Ecological Conservation and Restricted Zone with Potential Pollution had low NCPIs attributing to historical or natural factors. Our study implies that the classified zones can provide fundamental and momentous information for establishing appropriate priorities of heavy metal risk management and control.

Spatio-temporal variation and source changes of potentially toxic elements in soil on a typical plain of the Yangtze River Delta, China (2002-2012).

The spatio-temporal variation and temporal changes in the sources of Cr, Pb, Cd, Hg, and As in soil on the Hangzhou-Jiaxing-Huzhou (H-J-H) Plain were analysed based on 4,359 soil samples collected in 2002 and 2012. Geostatistical and spatial analysis methods were used to explore the spatio-temporal variation in the pollution levels and 'pollution hotspots' for potentially toxic elements (PTEs), and the positive matrix factor model was used to quantitatively appoint and analyse temporal changes in PTE sources. The results indicated that the PTE content in most parts of the survey area were at a safe level in both 2002 and 2012, but a clearly upward trend was detected for Cr, Pb, and Cd. Moreover the pollution index for Cr, Pb, Cd, and the Nemerow composite pollution index increased in the west but decreased in the east of the H-J-H Plain from 2002 to 2012. The pollution index for Hg and As presented the opposite spatial pattern. It is obvious that there have been changes in the spatial pattern of pollution hotspots for PTEs on the H-J-H Plain from 2002 to 2012. Four sources of PTEs in soil were quantitatively appointed. In 2002, 2012, the dominant sources of Cr, Cd, Hg, and As were soil parent materials, industrial activities, atmospheric deposition and agricultural inputs, respectively. The dominant source of Pb in the soil changed from traffic emissions to soil parent materials, indicating the benefit of banning the use of leaded gasoline in China. This study highlights the importance of monitoring soil environmental quality and highlights the significance of spatio-temporal variation in PTEs in suburban zones or transitional areas undergoing rapid industrialization and urbanization, like the H-J-H Plain.

Estimating spatial and temporal variation in ocean surface pCO2 in the Gulf of Mexico using remote sensing and machine learning techniques.

Research on the carbon cycle of coastal marine systems has been of wide concern recently. Accurate knowledge of the temporal and spatial distributions of sea-surface partial pressure (pCO2) can reflect the seasonal and spatial heterogeneity of CO2 flux and is, therefore, essential for quantifying the ocean's role in carbon cycling. However, it is difficult to use one model to estimate pCO2 and determine its controlling variables for an entire region due to the prominent spatiotemporal heterogeneity of pCO2 in coastal areas. Cubist is a commonly-used model for zoning; thus, it can be applied to the estimation and regional analysis of pCO2 in the Gulf of Mexico (GOM). A cubist model integrated with satellite images was used here to estimate pCO2 in the GOM, a river-dominated coastal area, using satellite products, including chlorophyll-a concentration (Chl-a), sea-surface temperature (SST) and salinity (SSS), and the diffuse attenuation coefficient at 490 nm (Kd-490). The model was based on a semi-mechanistic model and integrated the high-accuracy advantages of machine learning methods. The overall performance showed a root mean square error (RMSE) of 8.42 µatm with a coefficient of determination (R2) of 0.87. Based on the heterogeneity of environmental factors, the GOM area was divided into 6 sub-regions, consisting estuaries, near-shores, and open seas, reflecting a gradient distribution of pCO2. Factor importance and correlation analyses showed that salinity, chlorophyll-a, and temperature are the main controlling environmental variables of pCO2, corresponding to both biological and physical effects. Seasonal changes in the GOM region were also analyzed and explained by changes in the environmental variables. Therefore, considering both high accuracy and interpretability, the cubist-based model was an ideal method for pCO2 estimation and spatiotemporal heterogeneity analysis.

Current status, spatial features, health risks, and potential driving factors of soil heavy metal pollution in China at province level.

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.