Existing approaches for assessing soil heavy metal(oid)s (HMs) accumulation at the regional scale remain limited in their ability to consistently link spatiotemporal input and output processes and to accurately quantify fluxes without relying on extensive environmental monitoring data. To address these limitations, we developed an integrated modeling framework that couples a bottom-up multi-source emission inventory, the AERMOD atmospheric dispersion model, and a HYDRUS-based water-solute transport module, further extended with established empirical approaches to quantify output processes, enabling long-term, spatially explicit simulation of soil HMs dynamics, with a machine learning module incorporated to calibrate process-derived net HMs fluxes. The framework was applied to an industrial agglomeration located in eastern coastal Zhejiang province, China. Results indicated that, with rapid industrialization and urbanization, atmospheric deposition inputs have steadily increased, whereas agricultural inputs have declined, with high-deposition zones concentrated within 10 km away from the point source. Simulation of output processes revealed that leaching and soil erosion were the dominant pathways of HMs loss, while runoff fluxes exhibited a gradual increase in response to growing impervious surface. A pronounced decline in soil environmental carrying capacity was predicted after 50 years, with overload zones expanding substantially, particularly for As (30.82 %), Cu (31.04 %), Cd (19.96 %), Pb (28.48 %) and Hg (32.66 %). Risks were dominated by As and Cu in forestland due to their high ecotoxicity, while the carrying capacity declined sharply in cultivated and residential land owing to receptor sensitivity and intensive human activities. This framework provides a robust tool for tracing emission sources, elucidating transport and accumulation dynamics, and supporting long-term risk zoning and early warning for regional environmental management.

Traditional health risk assessment (HRA) generally overlooks bioaccessibility, potentially leading to overestimated risk estimates. In this study, we systematically investigated the heavy metal(oid)s (HMs) characteristics of 126 soil samples collected from an industrial agglomeration area, based on which, in vitro assays were conducted to test bioaccessible HMs concentrations, and further integrated into both probabilistic HRA and the prediction of internal exposure levels. The study results of probabilistic HRA indicated that both scenarios showed negligible non-carcinogenic risks (with and without bioaccessibility), but the probabilities of non-carcinogenic risks could decrease by 82.83 % for adults and 78.93 % for children when considering bioaccessibility, along with a significant reduction on the overestimation of carcinogenic risks by approximately 70 %. In addition, internal exposure levels predicted by a physiologically based toxicokinetic (PBTK) model demonstrated that incorporating bioaccessibility led to a substantial reduction in urinary HMs concentrations, with decreases ranging from 60.41 % for Cd to 98.50 % for Cr. Moreover, the deviations from the verification value were consistently lower and more stable across all metals and age groups, indicating that incorporating bioaccessibility into risk assessments provides a more accurate and reliable estimation of HMs exposure to avoid overestimation of associated health risks, and to support more informed decision-making in soil environmental health management.

Heavy metal(oid)s contamination in soil is a worldwide concerned issue, considering the potentially far-reaching hazards to ecosystem safe and human health. This study provides a comprehensive and systematic review on health risk assessment associated with soil HMs, and carries out a bibliometric analysis in terms of publication years, case distribution, land use characteristics, citation frequency and assessment models. The findings provide valuable knowledge for understanding research status, hotspots, limitations and future direction in assessing human health risks caused by soil HMs, revealing the rapid development and wide concern on this subject with 930 original articles across 67 countries, covering 21 HMs in 7 land use patterns. However, there is an urgent need for addressing uncertainties in quantifying the intricate relationship between HMs contamination and human health, which highlights the significance of probabilistic assessment methods, localized model parameters as well as the incorporation of bioaccessibility. This study contributes to enhance all-round understanding for soil HMs-related health risk assessment, and has broader prospects for performing a more precise and reliable health risk assessment to guide effective risk management.

With the increasing concern on soil pollution in context of land market reform, it’s an emerging topic to discuss whether soil pollution can cause land economic value substantially depreciated at geospatial scale. This study proposed a data science approach by synthesizing machine learning and deep learning algorithms to establish the land economic equivalent model (LEEM) with best prediction ability. The results indicated that Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) performed well in estimating residential/commercial and industrial land value, respectively. Spatial variation of soil environment quality indeed largely affected economic productivity of residential/commercial land with contribution of 13.82%. For industrial land, population density was strongly related to the land price (11.77%), of which importance score was much higher than other 10 variables except distance to center business district (CBD, 28.75%) and transaction time (28.61%). Another important finding was that soil protection measures could generate extra benefits equivalent with a mean of 22.46% and 28.92% for residential/commercial and industrial land value in current status, respectively, and high-value areas could also be predicted for effective decisions on land allocation and trading. In terms of sustainable land management, the regression tree-based LEEM is an effective tool in assisting decision makers with urban land reclamation, planning and pricing.

The performance of contaminated site management in terms of its sustainability can be inherently uncertain, considering that this performance depends not only on individual site characteristics and variability, but also on integrated (and potentially long-term) environmental, social, economic and technical impacts. The present study proposes a combination weights-based TOPSIS model (Technique for Order Preference by Similarity to Ideal Solution), following a multi-criteria decision analysis (MCDA) approach, to quantitatively analyze the most influential measures and indices over the entire life cycle of contaminated site management (from site investigation to land reuse) in China. Results indicate that the sustainability performance of different sites varies widely due to differences in the best management practices (BMPs) implemented. Environmental dimensions in strategy design and remediation implementation processes contribute most to the overall sustainability performance when whole life-cycle management is considered, with social dimensions relatively under-emphasised. Based on a sensitivity analysis of the TOPSIS method, random variations of indices' weights did not result in significant alteration of the modelled sustainability performance of the evaluated sites, which indicates that the method provides a relatively robust approach. The MCDA approach developed enables stakeholders to adopt optimal BMPs for risk control and to enhance sustainability performance in contaminated site management, covering the whole life cycle of site remediation and redevelopment, although further work is required to determine its international applicability.

Currently, site contamination is considered to be a sustained, international environmental challenge, and there is an urgent practical need to build a core theoretical system and technical methodology for the sustainable risk management of soil contamination, together with its prevention and control. We aim to improve the risk management of contaminated sites in the post-remediation era, in line with the current trend of sustainable development. The work is based on the theory of sustainability science and the eco-environmental zoning system., In this study, we build a conceptual model that can be used to classify the sustainable performance of contaminated sites in terms of risk management in line with the existing environmental management system for contaminated sites in China. To provide a scientific decision-making basis and technical support for the refined classification management of soil environments in China during the 14th Five-Year Plan period, five typical contaminated sites were selected for a quantitative evaluation by applying multi-technical approaches, including sociological, economic and statistical methods. The results showed that the sustainable performance of contaminated sites with regard to management was affected not only by pollution risk factors but also by potential utility benefits. Specified management strategies should be developed according to different levels of sustainability so as to achieve the goals of improving land use efficiency and enhancing urban functions.

Heavy metal pollution of soils in industrial agglomeration areas is an increasing concern worldwide. In this study, we traced the sources of heavy metal emissions using a positive matrix factorization (PMF) model. Accordingly, we proposed a novel static-dynamic risk interaction model incorporating multiple risk-related factors to quantify the spatial interaction of emission sources and the probability of accumulation of heavy metals on a large scale. This model was further classified using the Jenks optimization technique to predict the spatial distribution of high-risk hotspots. Our results determined four primary emission sources of heavy metals: industrial (35.01 %), natural (28.61 %), agricultural (26.07 %), and traffic (10.31 %) sources. Five levels were classified by the integrated risk coefficient (IRC), namely, from extremely high to extremely low risk. The extremely high- and high-risk hotspots constituting 41.52 % of the total area of the Zhenhai District, with IRC values ranging from 0.221 to 0.413, were mainly generated by multiple sources linked to PMF-based factors. This quantitative evaluation framework can generate a high-resolution spatially distributed pollution risk map at the grid scale (1 km), which can provide a relatively precise basis for policymaking for point-to-point soil pollution management.

The presence of contaminated land is an inevitable legacy of industrial activity, and the management decisions governing reclamation of this land are key in minimizing environmental risk and allowing safe and effective land reuse. In this context, to predict the optimal remediation options for future decision-making processes in sus-tainable site management, thus enhancing information communication between stakeholders, 17 decision sensitivity parameters are analyzed in this study and their influence on the management patterns of contami-nated sites identified with three decision tree (DT) algorithms including C4.5 (successor of Iterative Dichotomiser 3/ID 3), CHAID (Chi-squared Automatic Interaction Detection), and CART (Classification and Regression Trees), which is the first attempt to use artificial intelligence technology to predict strategy-based decision-making for contaminated site management. Based on four performance metrics (accuracy, precision, recall ratio and F1 score), CART-based DT model shows the highest prediction accuracy at an average value of 78.57%, which indicates a relatively credible decision simulation to assist in more efficient contaminated site management. With regard to specific factors and influence mechanisms on contaminated site management, the results demonstrate 7 recognition rules corresponding to 6 driving factors which have the greatest influence on the decision-making process. Long-term monitoring time, the type of land reuse and ex-situ performance are the most important factors in determining field implementation of cleanup activities. The built decision tree model and induced decision rules, once well-trained, can be relied on for a sustainable site management strategy as data become available at a new site.

As an emerging approach that aims to control, mitigate or prevent the risk of contaminated sites by cutting off the transmission routes of pollutants, risk control method has become an economic choice for researchers and governors to manage the contaminated sites. However, the public's acceptance is at a relatively low level, and investigation into how to increase this is needed. In this study, a focus group interview was conducted to identify the dimensions of the public's risk and benefit perception regarding the use of risk control method for contaminated sites. Using data from an online survey of 418 residents in Tianjin, a city in desperate need of this method for contaminated sites, structural equation modeling was used to examine the influence mechanism of public's knowledge of soil pollution on their acceptance of it. The results included the following points: (1) The improvement of the public's soil pollution knowledge causes the overestimation of risks and the underestimation of benefits regarding risk control method, which then decrease of their acceptance of it. (2) knowledge triggered emotional judgement rather than rational judgement works in their decision-making process. The study concludes that science-based propaganda should be strengthened to eliminate residents' concerns about the environment, health, and the government's regulations, and their negative emotion should be specially taken into account when developing projects using this method. This study innovatively explored the public's cognitive mechanisms in relation to risk control method from both rational and emotional perspectives.

土壤环境承载力研究是"土十条"中加强土壤污染防治研究的重要内容,在场地土壤污染修复目标值确定方法中耦合土壤环境承载力的估算模型能够极大地提升修复目标值制定的科学性.以江苏省某市废弃化工场地为研究对象,针对场地土壤中的三种主要污染物汞、六氯苯和氯苯,通过场地土壤布点采样分析,进行了污染物浓度空间分布特征分析和健康风险评价,并基于土壤环境承载力计算公式进行了三类目标污染物的土壤环境承载力和修复目标值的估算.研究结果表明,目标场地土壤中汞和六氯苯浓度超过筛选值的样点占到总样点的50％以上,而氯苯浓度超过筛选值的样点占到了总样点的17％,其空间分布主要受生产过程中的污染源的分布及生产工艺影响;土壤汞和氯苯存在较为严重的非致癌风险,而六氯苯存在严重的致癌风险;以风险筛选值作为环境质量标准的一般情景下的三种污染物的土壤环境承载力均有样点出现了小于0,即超过承载力的现象;以风险管控值为环境质量标准的乐观情景下,该三种污染物的环境承载力均大于0,即该区域还具备继续吸纳污染物的能力;基于土壤环境承载力估算模型的修复目标值较对应的风险筛选值和管控值均高出1.8倍～1.9倍,这是由于在承载力估算模型中对风险产生过程以及土壤对污染物吸附固定过程进行了系数校正的原因.以上研究结果为污染物的土壤环境承载力研究的发展及应用提供了思路和技术方法.

以可持续发展理论和风险管理理念为基本原则的污染场地可持续风险管控已成为当前国际社会场地管理的重要决策问题和研究前沿热点,为明确影响污染场地风险管控可持续发展能力的决定性因素,基于国内外污染场地可持续风险管控相关文献的系统调研,构建契合我国场地管理背景的区域污染场地风险管控可持续评价体系,通过指标综合权重计算判定影响风险管控可持续发展能力的关键因子.结果表明,区域尺度上可能影响我国污染场地风险管控可持续性的指标多达44个,涵盖环境、社会、经济和技术这4个维度,单指标影响程度为0.26％～5.01％(平均值2.27％).潜在风险和温室气体排放(环境指标)、健康与安全和公众参与(社会指标)、管控成本和环保投资(经济指标)、修复周期和修复效果(技术指标)是影响我国污染场地风险管控可持续发展的重要因子,影响程度为1.89％～5.01％(平均值3.58％).具有较强政策敏感性的指标,包括考核指标、投融资创新、名录管理、能力建设、安全利用和制度建设等,对风险管控可持续性已经产生1.18％～3.48％的正向影响,随着政策制度的深入落实与全面地域推广,其对风险管控可持续发展的助力效应将更加明显.

以风险管理为基本原则的污染场地可持续治理修复及安全利用已成为全球范围内一个紧迫的环境和发展问题.为促进快速、经济、有效的风险管控技术在我国土壤污染防治初期阶段的广阔应用,本文在明确污染场地风险管控广义和狭义内涵属性的基础上,系统阐述了工程控制、制度控制和监测自然衰减等狭义风险管控技术的技术原理、工程应用和适用条件,剖析英美等发达国家风险管控体系并结合我国土壤环境管理实际建立技术体系和政策体系支撑下的风险管控模式,最后针对我国污染场地风险管理基础薄弱、风险管控体系不健全和风险管控技术支撑不足等问题,提出基于绿色可持续理念和全生命周期管理理论的"防、控、治、管"四位一体的污染场地风险管控总体布局,对于推动我国风险管控技术应用和提高场地风险管理水平具有重要的现实意义.

The worldwide diversity of contaminated sites, coupled with a scarcity of available land in urban spatial planning, has led to an increasing political significance for brownfield conservation and re-use to achieve land resource sustainability. In this study, economic or so-called rebound effects of land regeneration, are studied via a global meta-analysis on value fluctuation of surrounding property. To this end, a total of 91 observations from 28 HPM (Hedonic Pricing Model) studies were synthesized to conduct a meta-analysis following a conditional random-effects procedure. The empirical results indicate that, in line with expectations, the conservation and recycling of land resource indeed generate significant rebound in the implicit price of residential houses, especially for those located within 2 kilometers of contaminated sites. Before land remediation and re-use, dwellings closest in distance to contaminated sites experience the greatest value loss. On average, the depreciation in property values within the first 1km distance from a contaminated site is about 8.18%, significantly at the 1% level, while the corresponding adverse impact from 1 to 2 km distance is a 4.8% price premium significantly at the 5% level. The significance of the stigma or rebound effects depend on 12 attributes, in which, house age, location, FAR (Floor Area Ratio) and CBD (Central Business District) variables have the largest impact, of -37.38%~37.5%. From a practical perspective, the findings of this meta-analysis: 1) help refine contributing parameters in HPM studies to evaluate environmental economics; and 2) provide meaningful decision-making support for cost-effective remediation and benefit maximization.

Sustainable remediation, which promotes the use of more sustainable practices during environmental clean-up activities, is an area of intense international development. While numerous indicators related to sustainable remediation assessment have been utilized and published in related academic literature, they are difficult to unify and vary in emphasis between countries. Following literature retrieval from CNKI, Springer, ScienceDirect, and Wiley Online databases, we present a systematic and bibliometric analysis of relevant national and international literature to define the most frequently considered indicators of sustainability, which play important roles in selecting remediation technologies or site management methods from a sustainability perspective. Following the application of co-occurrence analysis and social network analysis, the results indicate that 1) environmental criteria are most commonly used in evaluating remediation technologies, with significantly less emphasis on social criteria in Chinese publications in particular; 2) with an increasing number of publications in the last 20 years, sustainable remediation has gone through an initial stage, rising stage, and burst or wider adoption stage, characterized by a transformation of the research theme from a predominantly risk-based management approach to a sustainability-based one, with risk management as an underpinning principle; 3) health, resource, cost, and time are the most widely used indicators in terms of social, environmental, economic, and technical criteria, respectively; 4) clear differences exist between China and other nations, particularly in the frequency of usage of each indicator, the application of social criteria, and preferred stakeholders. Nevertheless, China has made significant progress and now makes increasing contributions to sustainable remediation at an international level.

It is widely acknowledged that a simplified and robust approach to evaluating thecombined effects of chemical mixtures is critical for ecological risk assessment (ERA) of contaminated soil. The earthworm (Eisenia fetida) was used as a model to study the combined effects of polymetallic contamination and the herbicide siduron in field soil using a microcosm experiment. The responses of multiple biomarkers, including the activities of catalase (CAT), superoxide dismutase (SOD), glutathione reductase (GR) and acetylcholine esterase (AChE), the concentrations of glycogen, soluble protein (SP), malonaldehyde (MDA), and metallothionein (MT), and the neutral red uptake test (NRU), were investigated. Multivariate analysis, Principal Component Analysis (PCA) and Spearman's Rank Correlations analysis (BVSTEP) revealed that the activities of AChE and CAT and the NRU content were the prognostic biomarkers capturing the minimum data set of all the variables. Internal Cd (tissue Cd) in earthworms was closely related to the health status of worms under combined contamination of heavy metals and siduron. The integrated effect (Emix) calculated based on the activities of AChE and CAT and NRU content using the stress index method had significantly linear regression with internal Cd (p<0.01). Emix(10), Emix(20), and Emix(50) were then calculated, at 1.27, 1.63 and 2.71 mg/kg dry weight, respectively. It could be concluded that a bioassay-based approach incorporating multivariate analysis and internal dose was pragmatic and applicable to evaluating combined effects of chemical mixtures in soils under the guidance of the top-down evaluation concept of combined toxicity.

The knowledge of soil environmental quality and its changing trends is important for safe and sustainable land utilization. However, comprehensive information on soil environment carrying capacity, involving environmental, economic and social pressures, is relatively rare. In this study, a modified dynamic capacity model is developed to estimate soil environment carrying capacity in terms of a combined consideration of soil environment capacity, cumulative input/output rate and risk characteristics. Based on the method proposed, this paper demonstrates the current pollution status and remaining soil capacity of the Beijing urban area, and establishes a conceptual “early warning” model for soil environmental quality, to predict time-dependent changing patterns of soil pollutants under different accumulation scenarios. The results showed that for Beijing soil environmental carrying capacity varied with land use type and pollutant. Compared with Cu, Zn and Pb, Cd posed the greatest threat to soil environmental carrying capacity in both residential areas and green parks. Heavy metal carrying capacity in soils in built-up areas in Beijing was not overloaded currently, and will not deteriorate significantly over the short-to medium-term in a hypothetical “decreased input” scenario. The method proposed provides a simple, cost-effective, and quantitative tool for mapping soil quality level, and assessing the need for risk management measures, in China and elsewhere.

Early warning of soil environmental quality is an important basis for implementing classified and graded soil risk management measures. To quickly understand the regional soil environmental quality and take effective measures in time to prevent continuous soil pollution before deterioration of soil environmental quality, a simple, effective, and quantifiable early warning system for soil environmental quality of agricultural land and development land was respectively established based on environmental capacity and pollutant input-output flux theory. Furthermore, corresponding method and mechanism for early warning were defined based on soil environmental quality standards, food safety standards, and carcinogenic risk coefficients. The agricultural land in Youxian county and the development area within the fifth-ring in Beijing were chosen to assess the soil environmental quality and predict risks of heavy metals exceeding standards in different scenarios. The results show that the soil environmental quality of the agricultural land in Wangling and Taoshui Town both can be classified to the fifth early warning level. Compared with other remediation measures, the Cd contents of soil can be lowered to risk screening levels in the short term by the scenario of "paddy straw not returned to the field". The soil quality in the development area within the fifth-ring in Beijing belongs to the first early warning level under both the "no intervention" and the "decreased input" scenarios, which means that Cd, Cu, Pb, and Zn all need more than 50 years to reach their threshold values to pose potential health risks.

To understand the effect of soil environmental carrying capacity on pollutants and human activities, as well as to effectively prevent the aggravation of soil pollution and control soil environmental risks, a comprehensive indicator system for soil environmental carrying capacity is developed by analyzing the input-output flux and risk characteristics of soil pollutants. Furthermore, an evaluation method for soil environmental carrying capacity is proposed by defining safety coefficients related to evaluation indicators. Based on evaluation of soil environmental quality, the system reflects soil properties, pollution evolution trends, and risk characteristics, focusing on the soil buffering function. Further, a quantitative evaluation is carried out to assess the regional soil environmental carrying capacity of heavy metals on development land in Beijing. The results show that the soil environmental carrying capacity of Cu, Zn, Pb, and Cd in Beijing varies widely. The soil environmental carrying capacity of Cd is much lower than that of other elements. Four policy recommendations are proposed as significant for effective soil pollution prevention and control:clarifying concepts for soil environmental carrying capacity, improving the evaluation framework, constructing an information database, and implementing demonstration pilots.

It is fundamental to sustainable industrial development that resource flows keep continuous trait from nature to industrial ecosystem and back to natural cycles within environmental carrying capacity. Traditional separated management method leads to unsustainable resource flows due to the linear flows, which are the root cause for severe environmental problems and resources shortage. Shifting the intermittent or linear trait of the resource flows to more continuous and circular flows is a tremendous challenge in the industrial ecosystem for the sustainable management of resources. Existing research mainly focuses on improving the efficiency of resource use or reducing waste, but there is a lack of systematic management research on resource flows from the perspective of the industrial ecosystem. Thus, we explore a life cycle management (LCM) approach based on life cycle thinking and the industrial symbiosis mechanism. By clarifying the life cycle system of resource flows and the symbiosis pattern, we propose the framework of LCM for cyclic resource flows. Based on the framework, the symbiosis-based life cycle model of resource flows is established and the integrated assessment method that supports the life cycle targeted management from environmental impact and sustainable use perspectives is developed. The case study demonstrates the ability of the approach to help decision makers identify the key issues and generate the targeted integrated strategies for facilitating the sustainable resource flows in the industrial ecosystem.