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

Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework

Surface ozone has become a significant atmospheric pollutant in China, exerting a profound impact on crop production and posing a serious threat to food security. Previous studies have extensively explored the physiological mechanisms of ozone damage to plants. However, the effects of ozone interactions with other environmental factors, such as climate change, on agricultural productivity at the regional scale, particularly under natural conditions, remain insufficiently understood. In this study, we employed an interpretable machine learning framework, specifically the eXtreme Gradient Boosting (XGBoost) algorithm enhanced by SHapley Additive exPlanations (SHAP), to investigate the influence of ozone and its interactions with environmental factors on crop production in China's primary winter wheat region. Additionally, a structural equation model was developed to elucidate the mechanisms driving these interactions. Our findings demonstrate that ozone pollution exerts a significant negative effect on winter wheat productivity (r = -0.47, P < 0.001), with productivity losses escalating from -12.28 % to -22.09 % as ozone levels increase. Notably, the impact of ozone is spatially heterogeneous, with western Shandong province identified as a hotspot for ozone-induced damage. Furthermore, our results confirm the complexity of the relationship between ozone pollution and agricultural productivity, which is influenced by multiple interacting environmental factors. Specifically, we found that severe ozone pollution, when combined with high aerosol concentrations or elevated temperatures, significantly exacerbates crop productivity losses, although drought conditions can partially mitigate these adverse effects. Our study highlights the importance of incorporating the interactive effects of air pollution and climate change into future crop models. The comprehensive framework developed in this study, which integrates statistical modeling with explainable machine learning, provides a valuable methodological reference for quantitatively assessing the impact of air pollution on crop productivity at a regional scale.

Understanding the driving mechanisms of site contamination in China through a data-driven approach

China currently faces significant environmental risks stemming from contaminated sites. The driving mechanism of site contamination, influenced by various drivers, remain obscured due to a dearth of quantitative methodologies and comprehensive data. Here, we used a data-driven causality inference approach to construct an interpretable random forest (RF) model. Results show that: (1) the trained RF model demonstrated remarkable predictive accuracy for identifying contaminated sites, with an accuracy rate of 0.89. In contrast to conventional correlation analysis, the RF model excels in discerning the key drivers through non-linear and genuine causal relationships between these drivers and site contamination. (2) Among the 25 potential drivers, we identified 18 key drivers of site contamination. These drivers encompass a broad spectrum of factors, including production and operational data, pollutant control level, site protection capability, pollutant characteristics, and physical-geographical conditions. (3) Each key driver exerts varying impacts on site pollution, with diverse directions, intensities, and underlying patterns. The partial dependence plots (PDPs) illuminate the role of each key driver, its critical value contributing to site pollution, and the interplay between these drivers. The key drivers facilitate the realization of three primary contamination processes: uncontrolled release, effective migration, and persistent accumulation. In light of our findings, environmental managers can proactively prevent site contamination by regulating single, dual, and multiple key drivers to disrupt critical pollution processes. This research offers valuable insights for devising targeted strategies and interventions aimed at mitigating environmental risks associated with contaminated sites in China.

Development of a Multifaceted Perspective for Systematic Analysis, Assessment, and Performance for Environmental Standards of Contaminated Sites

Contaminated soil and groundwater can pose significant risks to human health and ecological environments, making the remediation of contaminated sites a pressing and sustained challenge. It is significant to identify key performance indicators and advance environmental management standards of contaminated sites. The traditional study currently focuses on the inflexible collection of related files and displays configurable limitations regarding integrated assessment and in-depth analysis of published standards. In addition, there is a relative lack of research focusing on the analysis of different types of standard documents. Herein, we introduce a cross-systematic retrospective and review for the development of standards of the contaminated sites, including the comprehensive framework, multifaceted analysis, and improved suggestion of soil and groundwater standards related to the environment. The classification and structural characteristics of different types of files are systematically analyzed of over 300 national, trade, local, and group standards for the contaminated sites. It exhibits that trade standards are the main types and testing methods are the important format within numerical considerations of soil standards. The guide standard serves as a crucial component in environmental management for investigating, assessing, and remediating of contaminated sites. Future improvement plans and development directions are proposed for advancing robust technical support for effective soil contamination prevention and control. This multidimensional analysis and the accompanying suggestions can provide improved guidance for Chinese environmental management of contaminated sites and sparkle the application of standards in a wide range of countries.

Determining the soil odor control area: A case study of an abandoned organophosphorus pesticide factory in China

Currently, soil odor-active substance screening and evaluation methods for contaminated sites are underdeveloped, with unclear treatment objectives and areas. Consequently, some sites suffer from odor issues during and even after remediation. This study focused on an organophosphorus pesticide factory site in Guangdong Province, China. It established a method of determining the odorant control area using a comprehensive approach combining instrumental and olfactory soil sample analyses. The main odor-active substances identified were ethylbenzene, phenol, m, p-xylene, styrene, toluene, and o-xylene, with odorant control values (the limit of odor-active substance contents) of 35.2, 28.1, 8.0, 11.3, 40.2 and 89.3 mg/kg respectively. Instrumental analysis of soil samples revealed 11 sampling points where the main odor-causing substances exceeded standard levels. Among the substances, ethylbenzene (1.48E+04 mg/kg) had the highest content, exceeding the limit up to 421-fold. Olfactory analysis indicated 14 sampling points with odor intensity surpassing the standard (OI > 2). Based on the instrumental analysis results and the odorant control value, the initial estimated odor control area (area with the risk of odor nuisance) was 5.64E+03 m2. Incorporating the olfactory analysis findings, the control area was adjusted by 1.25E+03 m2, leading to a final calculated soil odor control area of 6.89E+03 m2 for the study site. The comprehensive approach to analyzing soil samples for odor control can help evaluate the extent of soil odor pollution in contaminated sites and provide a scientific basis for effectively removing and managing odor-causing substances in soil.

Spatiotemporal assessment and scenario simulation of the risk potential of industrial sites at the regional scale

Spatiotemporal risk and future evolutionary distribution characteristics of industrial sites are crucial for regional environmental supervision. However, traditional site survey methods have long cycles, high costs, and small coverage and usually only consider the static risk of a single industrial site to a single receptor. Low-cost, large-scale, and long-term multi-source data can compensate for the shortcomings of traditional site surveys. Previous studies have rarely considered the spatiotemporal heterogeneity of industrial sites and assessed their dynamic risks at the regional scale. This study used China's Yangtze River Delta Urban Agglomeration as the study area. We assessed the risk potential of industrial sites from 2000 to 2020 using multi-source and multiperiod data. We also simulated the risk potential for 2030 and 2050 using a patch-generating land use simulation (PLUS) model under different scenarios. The results indicated that the proportion of medium- and high-risk potential grids from 2000 to 2020 ranged from 2.53 % to 5.61 % in the study area, with the vast majority of areas (94.39 %-97.47 %) having low- or no-risk potential. The PLUS model exhibited remarkable reliability from 2005 to 2020, with the overall accuracy, Kappa coefficient, and Moran's index ranging from 83 % to 89 %, 0.38 to 0.59, and 0.34 to 0.56, respectively. The future prediction results indicated that the number of high-risk potential grids (>5 %) showed an upward trend under natural development scenarios in 2030 and 2050 and a downward trend under the ten-chapter soil pollution action plan or strict control scenarios. This study provides vital information for addressing the challenges of industrial site management and environmental risks in similar regions.

Identifying multiple soil pollutions of potentially contaminated sites based on multi-gate mixture-of-experts network

With the rapid increase in the amount and sources of big data, using big data and machine learning methods to identify site soil pollution has become a research hotspot. However, previous studies that used basic information of sites as pollution identification indexes mainly have problems of low accuracy and efficiency when conducting complex model predictions for multiple soil pollution types. In this study, we collected the environmental data of 199 sites in 6 typical industries involving heavy metal and organic pollution. After feature fusion and selection, 10 indexes based on pollution sources and pathways were used to establish the soil pollution identification index system. The Multi-gate Mixture-of-Experts network (MMoE) were constructed to carry out the multi-tasks of soil heavy metals, VOCs and SVOCs pollution identification simultaneously. The SHAP framework was used to reveal the importance of pollution identification indexes on the multiple outputs of MMoE and obtain their driving factors. The results showed that the accuracies of MMoE model were 0.600, 0.783 and 0.850 for soil heavy metals, VOCs and SVOCs pollution identifications, respectively, which were 0-20 % higher than their accuracies of BP neural networks of single tasks. The indexes of raw material containing organic compounds, enterprise scale, soil pollution traces and industry types have the different significant importance on site soil pollutions. This study proposed a more efficient and accurate method to identify site soil pollutions and their driving factors, which offers a step towards realizing intelligent identification and risk control of site soil pollution globally.

A prospective ecological risk assessment method based on exposure and ecological scenarios (ERA-EES) to determine soil ecological risks around metal mining areas

Soil heavy metal (HM) contamination around metal mining areas (MMAs) is a global concern that requires a cost-effective ecological risk assessment (ERA) method for preventive management. Traditional ERAs, comparing environmental HM concentrations with benchmarks, are labor- and cost-intensive in field investigations and chemical analyses, which challenge the management demands of numerous MMAs. In this study, a prospective ecological risk assessment method based on exposure and ecological scenario (ERA-EES) was developed to predict the eco-risk levels (low/medium/high) around MMAs prior to field sampling. Five exposure scenario indicators related to soil HM exposure and three ecological scenario indicators reflecting the soil bioreceptor response were selected and combined with the analytic hierarchy process and fuzzy comprehensive evaluation methods for ERA-EES development. Case application and performance evaluation with 67 MMAs in China demonstrated that the ERA-EES method had an overall effective and conservative performance when referring to potential ecological risk index (PERI) levels, with an accuracy of 0.87, kappa coefficient of 0.7, and low or medium eco-risk levels in PERI classified to high levels in ERA-EES. Overall, the selected scenario indicators could efficiently reflect the risk levels of soil HM pollution from mining activities. Besides, more regulatory efforts should be paid to the MMAs of nonferrous metals, underground and long-term mining and those located in southern China. This work provided a convenient and cost-effective prospective ERA method under the trend of ERA being tiered and refined, facilitating the risk management of various MMAs.

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.

Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites

Owing to the rapid development of big data technology, use of machine learning methods to identify soil pollution of potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due to the difficulty in obtaining key indexes of site pollution sources and pathways, current methods have problems such as low accuracy of model predictions and insufficient scientific basis. In this study, we collected the environmental data of 199 PCS in 6 typical industries involving heavy metal and organic pollution. Then, 21 indexes based on basic information, potential for pollution from product and raw material, pollution control level, and migration capacity of soil pollutants were used to established the soil pollution identification index system. We fused the original indexes into the new feature subset with 11 indexes through the method of consolidation calculation. The new feature subset was then used to train machine learning models of random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), and tested to determine whether it improved the accuracy and precision of soil pollination identification models. The results of correlation analysis showed that the four new indexes created by feature fusion have the correlation with soil pollution is similar to the original indexes. The accuracies and precisions of three machine learning models trained on the new feature subset were 67.4%- 72.9% and 72.0%- 74.7%, which were 2.1%- 2.5% and 0.3%- 5.7% higher than these of the models trained on original indexes, respectively. When the PCS were divided into typical heavy metal and organic pollution sites according to the enterprise industries, the accuracy of the model trained on the two datasets for identifying soil heavy metal and organic pollution were significantly improve to approximately 80%. Owing to the imbalance in positive and negative samples in the prediction of soil organic pollution, the precisions of soil organic pollution identification models were 58%- 72.5%, which were significantly lower than their accuracies. According to the factors analysis based on the model interpretability of SHAP, most of the indexes of basic information, potential for pollution from product and raw material, and pollution control level had different degrees of impact on soil pollution. However, the indexes of migration capacity of soil pollutants had the least effect in the classification task of soil pollution identification of PCS. Among the indexes, traces of soil pollution, industrial utilization years/start-up time, pollution control risk scores and enterprise scale having the greatest effects on soil pollution with the mean SHAP values of 0.17-0.36, which reflected their contribution rate on soil pollution and could help to optimize the current index scoring of the technical regulation for identifying site soil pollution. This study provides a new technical method to identify soil pollution based on big data and machine learning methods, in addition to providing a reference and scientific basis for environmental management and soil pollution control of PCS.

Risk classification of contaminated sites - Comparison of the Swedish and the German method

To classify contaminated sites into different risk classes, many different methods exist in Europe and worldwide. However, no systematic comparison of European risk classification methods has been carried out so far to carve out the advantages and disadvantages of the methods and to homogenize them. To address this research gap, this study aims at comparing the Swedish Method for Inventories of Contaminated Sites (MIFO) with the German Individual Assessment of Contaminated Sites Method (EB) from the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG) regarding the risk class categorization of 51 contaminated sites. The results revealed that with the MIFO 39% fewer contaminated sites are assigned to risk classes 1 and 2 and thus, subject to remediation compared to the EB. Moreover, in comparison to the EB, the MIFO showed a lower comparability, traceability, and a larger room for interpretation, which could be related to the lack of a quantitative approach such as a point or ranking system in the MIFO. Hence, we recommend providing the MIFO and other methods that lack a quantitative approach with a point and/or ranking system, similar to the EB, to increase their objectivity for the risk class categorization of contaminated sites.

An Approach for Selecting a Model for the Assessment of Potentially Contaminated Sites

Assessment of potentially contaminated sites (PCS) can be expensive; hence, simple and less demanding methods and models are required. This work attempts to provide an approach that can aid in selecting the most appropriate model for the PCS. The developed method uses over 100 field site data to evaluate four test models (analytical/empirical) that provide the maximum plume length (L_max ), which is used as a principal model ranking quantity in this work. Analysis of site data shows that field plume length (L_f ) follows a log-normal distribution. Subsequently, L_max is delineated with respect to L_f using a threshold probability as underestimating, overestimating, and overly-overestimating. Akaike information criterion (AIC) and analytical hierarchy process (AHP) are considered to support the threshold approach results. The classical AIC is modified (to AIC_mod ) to fit the term represented by the difference between L_f and L_max . Additionally, the threshold factors as a product of subjective weights are added to the AIC_mod . Using L_f and L_max , the AIC_mod provides a distinct ranking of the test models. For the AHP approach, the goodness of fit, underestimation, overly overestimation, and model complexity are the four chosen criteria. Similar to AIC_mod , the AHP approach provides a distinct ranking of the test models. The final decision on the best fitting model can be made on user criteria following the scheme developed in this work.

Development of a new methodology for multifaceted assessment, analysis, and characterization of soil contamination

It is important to identify key performance and core progress features of soil contamination management practices. Traditional research currently focuses on numerical statistics of contaminated sites but exhibits structural limitations regarding cross-assessment and in-depth analysis of published findings. Herein, we report a multidimensional perspective to assess the environmental management performance of soil contamination via systematic and historical development of construction land risk control and remediation lists (RCRLs). The considered contaminated sites are mainly concentrated in Northern China, Yangtze River Delta, Pearl River Delta, and Sichuan-Chongqing regions. Monthly historical overviews indicate that most lists are updated 4-5 times within 32 months. Direct chemical-related industrial production results in the largest number of contaminated sites. Arsenic and lead are the most common heavy metals of concern in soil contamination. The fiscal revenue index exhibits the best positive performance in terms of the number of contaminated sites. By employing the site number, update frequency, and published contents of different calculation proportions, ten types of integrated assessment indicators (IAIs) are established to evaluate the environmental achievements in various provincial regions in regard to soil contamination protection. This multifaceted strategy can provide advanced guidance for Chinese environmental management and expand the application of soil pollution risk control and remediation in a wide range of countries.

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.

The probabilistic linguistic term sets based ORESTE method for risk evaluation in Fine-Kinney model with interactive risk factors

The Fine-Kinney model is a quantitative and effective method to identify and evaluate potential risks. The Fine-Kinney method has been widely used in practice, while the traditional Fine-Kinney method is difficult to access risk parameters precisely in practice. Besides, the current Fine-Kinney method fails to take into account the fact that decision makers are interrelated in practice. Further, the detailed relationships among the failure modes cannot be reflected in the conventional Fine-Kinney method during the risk priority process, especially in the case of uncertain information. To compensate these deficiencies, this paper proposes an extended Fine-Kinney framework by integrating ORESTE (organísation, rangement et Syn-thèse de données relarionnelles) (in French), Choquet integral, and Probabilistic Linguistic Term Sets (PLTSs). Firstly, the PLTSs are utilized to express the decision makers’ complex risk preference information. Then, the Choquet integral is used to integrate risk evaluation information, which can simulate the potential interaction relationships among individual risk evaluations of decision-makers. Next, an extended ORESTE based on the PLTSs method is used to obtain the priority of potential hazards, in which distance measure of PLTSs is applied to replace Besson’s ranks. Moreover, the PIR (preference, indifference, and incomparability) structure is constructed to describe the detailed relationships between potential hazards. Finally, an illustrative example is described to illustrate the proposed risk evaluation method. After that, the rationality and efficiency of the proposed method are tested through the comparison with other similar methods.

Identification and risk prediction of potentially contaminated sites in the Yangtze River Delta

Identification and risk prediction of potentially contaminated sites (PCS) are crucial for the management of contaminated sites. However, the identification and risk prediction methods of PCS are lacking at a regional scale. Here, we established the fuzzy matching algorithm based on the site's name for identifying PCS in the Yangtze River Delta (YRD) from 2000 to 2020. The results showed that PCS in the YRD increased by over ten times, from 336 in 2000 to 4191 in 2020. Socio-economic and physical geography drive the growth of PCS and its spatiotemporal distribution, while the former has a more significant impact than the latter. We also presented a risk probability zoning strategy based on the source-pathway-receptor model, and proposed the patch-generating land-use simulation model to predict the risk probability of PCS in 2030. The results of risk probability zoning from 2000 to 2020 indicated that the local government of the YRD has started to pay attention to PCS management and risk control while developing social and economic. The results of risk prediction demonstrated that the proportion of low-risk probability pixels is 96.1% in 2030. Therefore, the planned indicator in the Action Plan on contaminated sites established by the State Council of China can be achieved in the YRD. Our experience in identifying and predicting PCS can inform how the local government worldwide manages PCS and tackles future challenges of achieving the ambition of site pollution control.

Method on site-specific source apportionment of domestic soil pollution across China through public data mining: A case study on cadmium from non-ferrous industries

The lack of emission data of major Cd-emitting enterprises has long limited the source apportionment of soil cadmium (Cd). Non-ferrous metal enterprises (NMEs) contribute the most Cd emissions in China in recent years. We estimated the cumulative Cd emission of 8750 NMEs across China through public data collection and material balance methods for the first time. The results showed that the total Cd emissions were estimated at 133,177 tons, of which 78.68% contributed by zinc primary smelting and mining. The emission hotspots are mainly concentrated in the south of the Yangtze River, such as Nanling Mountain areas, Nanpan River Basin, and Jincheng River Basin, as well as a few parts of the North and Northwest China. Then a significant positive spatial correlation was furtherly detected between NMEs and soil Cd, except for secondary smelting enterprises. Moreover, the hotspots of soil Cd pollution caused by NMEs were identified across China. By promoting the accounting calibrator from annual emission intensity of regional (mainly provincial) scale to the cumulative emission of site-specific enterprise in its entire life cycle, this study realized the finer description of the spatial heterogeneity of Cd emission from non-ferrous industry on a large scale and make it possible to refine the reliability of follow-up site-specific source apportionment, by introducing the emission intensity instead of the enterprise sites density. Finally, a modified approach for the regional source apportionment of soil pollution was proposed to obtain a more realistic and precise drawing. The results pointed out key NMEs subcategories and the affected hotspots which require continuous strengthening of Cd-related rectification. This methodological framework is expected to contribute to the precise management and differential sources control of Cd pollution and can be further extended to other pollutants for the precise targeting of key industries and hotspots during source pollution control in the future.