Identification of natural and anthropogenic factors in the spatial pattern of Pb in the surface soil of the Nanliu River Basin.

The identification of source in potentially toxic elements (PTEs) in watershed soils is crucial for environmental management. This study addresses the knowledge gap in understanding the combined influence of natural and anthropogenic factors on the spatial pattern of lead (Pb) in the surface soil of the Nanliu River Basin. The main objective was to identify and quantify these influences using an integrated approach of ANOVA, correlation analysis, IDW interpolation, and Local Moran's I.

Economic evaluation of cadmium phytoextraction: Comparing accumulators using cost-efficiency indicators and scenario analysis.

The existing efficiency indicators for phytoremediation cannot fully characterise the economic costs and application potential of remediation plants. In this study, we proposed a new strategy for selecting remediation plants based on economic indicators (cost per unit of heavy metal removal). Based on field experiments and data collection, we conducted cost-benefit analyses under various scenarios to compare the application prospects of four cadmium (Cd)-accumulators (Hylotelephium spectabile, Sedum alfredii, Sedum plumbizincicola and tobacco) on slightly Cd-contaminated farmland soil.

Spatial distribution and risk assessment of heavy metal pollution from enterprises in China.

Heavy metal pollution (HMP) directly affects the safety of agricultural products, thereby impacting human health. Industrial emissions, as the main source of soil HMP in China, require in-depth research on their pollution risks. Based on national heavy metal (HM) enterprise data, this paper analyzes the spatial distribution characteristics of key enterprises involved in the HMP across the country.

Neglected pathways of heavy metal input into agricultural soil: Water-land migration of heavy metals due to flooding events.

Flooding, carrying sediments, inundates farmlands across the world due to extreme adverse weather conditions. The casualties and property damage associated with flooding are important direct impacts. However, there is currently insufficient understanding of the remobilization and distribution of heavy metals (HMs) caused by flooding.

Fe oxides simultaneously improve stability of Cd and carbon in paddy soil：The underlying influence at aggregate level.

Iron (Fe) oxides have a strong adsorption affinity for Cd and organic carbon (SOC). However, under alternate wet-dry (IF) condition,the influences of Fe oxides on the speciation and disrtribution of Cd and SOC in soil aggregates are unkown. In the present study, soils untreated (S), removed (S-Fe) or added (S+Fe) Fe oxide soils were blended with cadmium chloride solution and cultivated for 56 days under different moisture management practices.

Quantifying the impact of factors on soil available arsenic using machine learning.

Arsenic (As) can accumulate in edible plant parts and thus pose a serious threat to human health. Identifying the contributions of various factors to soil available As is crucial for evaluating environmental risks. However, research quantitatively assessing the importance of soil properties on available As is scarce.

Nonionic surfactant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic concentration-dependent performance, mechanism, and machine learning prediction.

The surfactant-enhanced bioremediation (SEBR) of organic-contaminated soil is a promising soil remediation technology, in which surfactants not only mobilize pollutants, but also alter the mobility of bacteria. However, the bacterial response and underlying mechanisms remain unclear. In this study, the effects and mechanisms of action of a selected nonionic surfactant (Tween 80) on Pseudomonas aeruginosa transport in soil and quartz sand were investigated.

Sorption of cadmium by layered double hydroxides: Performance, structure-related mechanisms, and sequestration stability assessment.

Layered double hydroxides (LDHs) have been recognized to have great potential for the treatment of heavy metals in wastewater and soil through various mechanisms. Isomorphic substitution is an important mechanism for the sorption of heavy metal cations with LDH reconstruction and highly stable product formation. However, sorption performance, structure-related relationships, and, more importantly, stability are still poorly understood.

Diurnal variation in the urban thermal environment and its relationship to human activities in China: a Tencent location-based service geographic big data perspective.

The main factor of the formation and deterioration in China's urban thermal environment is human activity, which is difficult to describe and measure. A new perspective on the effect of human activity on the urban thermal environment can be obtained by examining the interaction between location-based service (LBS) data and the urban thermal environment in China. However, relevant research is still limited.

Identifying driving factors of soil heavy metal at the mining area scale: Methods and practice.

Identifying driving factors is of great significance for understanding the mechanisms of soil pollution. In this study, a data processing method for driving factors was analyzed to explore the genesis of Arsenic (As) pollution in mining areas. The wind field that affects the atmospheric diffusion of pollutants was simulated using the standard k-ε model.

Health risk assessment of heavy metals based on source analysis and Monte Carlo in the downstream basin of the Zishui.

In this study, stone coal mines in the lower reaches of the Zijiang River were adopted as the research object. To analyze the spatial distribution, sources, and health risks of heavy metals in the surrounding soil of stone coal mines, 82 topsoil samples were collected in the study area, and the contents of 8 heavy metals including Cd, Hg, As, Cr, Pb, Cu, Ni, and Zn were determined. The spatial distribution of heavy metals was analyzed using ArcGIS, and the pollution sources of heavy metals were identified using Positive matrix factorization (PMF).

[Source Apportionment and Potential Ecological Risk Assessment of Soil Heavy Metals in Typical Industrial and Mining Towns in North China].

To understand the status of heavy metals in soils of typical industrial and mining towns and quantitatively analyze the potential sources, the contents of seven heavy metals (Cd, As, Pb, Cr, Cu, Ni, and Zn) in 150 surface soils in Xuanhua District, Zhangjiakou City, Hebei Province were collected and examined. The geoaccumulation index and potential ecological risk index methods were used to evaluate the heavy metal pollution status and potential ecological risk. Principal component analysis (PCA) and the positive matrix factorization (PMF) model were used to comprehensively analyze the pollution sources of seven heavy metals, and geostatistics was used to identify the high contribution areas of potential sources.

Long-term variations in external phosphorus inputs and riverine phosphorus export in a typical arid and semiarid irrigation watershed.

Excessive phosphorus (P) along with drained water from farmland in the arid and semiarid watersheds when entering into water bodies brings about serious environmental problems in the aquatic ecosystem. It is critical to explore variations in watershed P balance and the relationship between anthropogenic P input and riverine total phosphorus (TP) export in a typical irrigation watersheds. In this study, long-term anthropogenic P variations in Ulansuhai Nur watershed (UNW), a typical irrigation watershed in Yellow River basin, was investigated using a quantitative Net Anthropogenic Phosphorus Input (NAPI) budget model.

Challenges and opportunities for improving the environmental quality of cadmium-contaminated soil in China.

Considering the soil cadmium pollution problem, the Chinese government proposed to estimate the costs and practicality "to completely improve the soil quality by the middle of this century". This study analyzed the challenges in achieving this goal using biophysical data from 10 typical demonstration soil phytoextraction projects. The current annual phytoextraction efficiency was determined as 14.

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.

[Enrichment Characteristics, Source Apportionment, and Risk Assessment of Heavy Metals in the Industrial and Mining Area of Northern Guangdong Province].

In order to understand the heavy metal pollution of the industrial and mining area in northern Guangdong Province, topsoil samples (0-20 cm) from 209 sites in study area were collected, and the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were analyzed. The source and distribution characteristics of Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn in soil samples were analyzed using the geographic information system (GIS) and principal component analysis (PCA), and the geo-accumulation index and potential ecological hazard index were used to evaluate their pollution status and ecological risk. The results showed that:① Except Ni, all seven heavy metal elements exceeded the national soil background value but were below the pollution risk screening value.

Accurate Prediction of Soil Heavy Metal Pollution Using an Improved Machine Learning Method: A Case Study in the Pearl River Delta, China.

In traditional soil heavy metal (HM) pollution assessment, spatial interpolation analysis is often carried out on the limited sampling points in the study area to get the overall status of heavy metal pollution. Unfortunately, in many machine learning spatial information enhancement algorithms, the additional spatial information introduced fails to reflect the hierarchical heterogeneity of the study area. Therefore, we designed hierarchical regionalization labels based on three interpolation techniques (inverse distance weight, ordinary kriging, and trend surface interpolation) as new spatial covariates for a machine learning (ML) model.

Environmental quality standards for agricultural land in China: What should be improved on derivation methodology?

Soil pollution has caused increasingly widespread attention in China. The environmental risk threshold of pollutants is a yardstick to measure soil environmental quality. For decades, plenty of research on soil environmental quality standards (SEQSs) has been carried out, providing scientific basis for the investigation and supervision of soil environmental quality.

Research on health risk assessment of heavy metals in soil based on multi-factor source apportionment: A case study in Guangdong Province, China.

Environmental problems caused by heavy metal pollution in soil have attracted widespread attention worldwide. Identifying and quantifying the heavy metal pollution sources and risks is crucial for subsequent soil management. In this study, an integrated source-risk method for source apportionment and risk assessment based on the PMF model, the geodetector model and the health risk assessment model (HRA) was proposed and applied.

Thermal Remediation of Soil Contaminated with Polycyclic Aromatic Hydrocarbons: Pollutant Removal Process and Influence on Soil Functionality.

Thermal remediation has been widely used for the removal of polycyclic aromatic hydrocarbon (PAH) from contaminated soil. The method has a high removal rate for semi-volatile organic pollutants; however, soil functionality is affected by the method because of the alteration of the soil properties. In this study, experimental soil was impregnated with phenanthrene (Phe), pyrene (Pyr), and benzo(a)pyrene (BaP); after natural air-dry aging, the thermal remediation experiment was carried out, using a tube-furnace and thermal gravimetry-Fourier transform infrared (TG-FTIR) equipment.

Temporal and spatial differentiation characteristics of soil arsenic during the remediation process of Pteris vittata L. and Citrus reticulata Blanco intercropping.

The intercropping of hyperaccumulators and fruit trees has great application prospects owing to its environmental and economic benefits. However, the variation tendency and spatial distribution characteristics of pollutants in soil are unclear. A 19-month pot positioning experiment was conducted to clarify the spatio-temporal characteristics of arsenic (As) during Pteris vittata L.

Carbonate bedrock control of soil Cd background in Southwestern China: Its extent and influencing factors based on spatial analysis.

Previous studies have found that Cadmium (Cd) may be condensed during the processes of the weathering and soil formation over carbonate bedrock, which is a typical phenomenon in the southwest region of China. However, the extent of the high value background and the geographical factors influencing its spatial distribution are still unknown. This study collected data from the Multi-Purpose Regional Geochemical Survey (MPRGS) and the Regional Geochemistry-National Reconnaissance Program (RGNR) to investigate the extent and its controlling factors based on spatial analysis.

Prediction of high-risk areas of soil heavy metal pollution with multiple factors on a large scale in industrial agglomeration areas.

Heavy metals in soil are a great threat to ecosystems and human health. The rapid development of industrialization has created a serious risk of heavy metal pollution in soil. The study took the industrial-intensive Dahetan subbasin as the typical area.

Transforming approach for assessing the performance and applicability of rice arsenic contamination forecasting models based on regression and probability methods.

Probability models are preferred over regression models recently in contamination evaluation but lacking proper performance comparison between two model types. Linear regression, logistic regression, XGBoost-based regression, and probability models were built considering soil arsenic and certain soil physicochemical properties of 287 samples to predict arsenic in rice grains. The outputs of all models were binarily classified uniformly for comparison.