Antimony, beryllium, cobalt, and vanadium in urban park soils in Beijing: Machine learning-based source identification and health risk-based soil environmental criteria

New insights into assembly processes and driving factors of urban soil microbial community under environmental stress in Beijing

Rapid urbanization leads to drastic environmental changes, directly or indirectly affecting the structure and function of soil microbial communities. However, the ecological response of soil microbes to environmental stresses has not yet been fully explored. In this study, we used high-throughput sequencing to analyze the assembly mechanism and driving factors of soil microbial community under environmental stresses. The results indicated that environmental stresses significantly affected soil properties and the levels of beryllium, cobalt, antimony, and vanadium contamination in soil generally increased from the suburban areas toward the city core. The composition and distribution of soil microbial communities demonstrated clear differences under different levels of environmental stress, but there was no significant difference in microbial diversity. Random forest and partial least squares structural equation modeling results suggested that multiple factors influenced microbial diversity, but antimony was the key driver. The influence of environmental stress led to deterministic processes dominating microbial community assembly processes, which promoted the regional homogenization of soil microbes. Therefore, this study provides new insights into urban soil microbial management under environmental stresses.

Determining soil conservation strategies: Ecological risk thresholds of arsenic and the influence of soil properties

The establishment of ecological risk thresholds for arsenic (As) plays a pivotal role in developing soil conservation strategies. However, despite many studies regarding the toxicological profile of As, such thresholds varying by diverse soil properties have rarely been established. This study aims to address this gap by compiling and critically examining an extensive dataset of As toxicity data sourced from existing literature. Furthermore, to augment the existing information, experimental studies on As toxicity focusing on barley-root elongation were carried out across various soil types. The As concentrations varied from 12.01 to 437.25 mg/kg for the effective concentrations that inhibited 10% of barley-root growth (EC10). The present study applied a machine-learning approach to investigate the complex associations between the toxicity thresholds of As and diverse soil properties. The results revealed that Mn-/Fe-ox and clay content emerged as the most influential factors in predicting the EC10 contribution. Additionally, by using a species sensitivity distribution model and toxicity data from 21 different species, the hazardous concentration for x% of species (HCx) was calculated for four representative soil scenarios. The HC5 values for acidic, neutral, alkaline, and alkaline calcareous soils were 80, 47, 40, and 28 mg/kg, respectively. This study establishes an evidence-based methodology for deriving soil-specific guidance concerning As toxicity thresholds.

Improving prediction of soil heavy metal(loid) concentration by developing a combined Co-kriging and geographically and temporally weighted regression (GTWR) model

The study of heavy metal(loid) (HM) contamination in soil using extensive data obtained from published literature is an economical and convenient method. However, the uneven distribution of these data in time and space limits their direct applicability. Therefore, based on the concentration data obtained from the published literature (2000-2020), we investigated the relationship between soil HM accumulation and various anthropogenic activities, developed a hybrid model to predict soil HM concentrations, and then evaluated their ecological risks. The results demonstrated that various anthropogenic activities were the main cause of soil HM accumulation using Geographically and temporally weighted regression (GTWR) model. The hybrid Co-kriging + GTWR model, which incorporates two of the most influential auxiliary variables, can improve the accuracy and reliability of predicting HM concentrations. The predicted concentrations of eight HMs all exceeded the background values for soil environment in China. The results of the ecological risk assessment revealed that five HMs accounted for more than 90% of the area at the "High risk" level (RQ ≥ 1), with the descending order of Ni (100%) = Cu (100%) > As (98.73%) > Zn (95.50%) > Pb (94.90%). This study provides a novel approach to environmental pollution research using the published data.

Is Cladophora fracta an efficient tool of accumulating critical raw materials from wastewater and there a potential health risk of use of algae as organic fertilizer?

 In this study investigation of accumulations of critical raw materials (cobalt (Co), antimony (Sb), vanadium (V), lanthanum (La) and tungsten (W)) from wastewater by using C. fracta were aimed. Besides, assessment of the potential health risks in terms of the use of organic fertilizer obtained from the macroalga to be harvested from the treatment were also aimed. Highest Co, Sb, V, La and W accumulations by algae in reactor were 125±6.2%, 201.25±10%, 318.18±15%, 357.97±18%, and 500±25%, respectively. When compared with control, Co, Sb, V, La and W in algae increased 2.25, 3.01, 4.18, 4.58, and 6 times, respectively. The algae was very high bioaccumulative for Co and La. Highest MPI was calculated as 3.94. Non-carcinogenic risk of CRMs according to different exposure types (ingestion, inhalation, and dermal) were calculated for man, woman and child. There is not any non-carcinogenic risk from the investigated exposure ways of algae as organic fertilizer.

Enhancing phosphorus source apportionment in watersheds through species-specific analysis

Phosphorus (P) is a pivotal element responsible for triggering watershed eutrophication, and accurate source apportionment is a prerequisite for achieving the targeted prevention and control of P pollution. Current research predominantly emphasizes the allocation of total phosphorus (TP) loads from watershed pollution sources, with limited integration of source apportionment considering P species and their specific implications for eutrophication. This article conducts a retrospective analysis of the current state of research on watershed P source apportionment models, providing a comprehensive evaluation of three source apportionment methods, inventory analysis, diffusion models, and receptor models. Furthermore, a quantitative analysis of the impact of P species on watersheds is carried out, followed by the relationship between P species and the P source apportionment being critically clarified within watersheds. The study reveals that the impact of P on watershed eutrophication is highly dependent on P species, rather than absolute concentration of TP. Current research overlooking P species composition of pollution sources may render the acquired results of source apportionment incapable of assessing the impact of P sources on eutrophication accurately. In order to enhance the accuracy of watershed P pollution source apportionment, the following prospectives are recommended: (1) quantifying the P species composition of typical pollution sources; (2) revealing the mechanisms governing the migration and transformation of P species in watersheds; (3) expanding the application of traditional models and introducing novel methods to achieve quantitative source apportionment specifically for P species. Conducting source apportionment of specific species within a watershed contributes to a deeper understanding of P migration and transformation, enhancing the precise of management of P pollution sources and facilitating the targeted recovery of P resources.

Comparison of antimony and arsenic behaviour at the river-lake junction in the middle of the Yangtze River Basin

As typical metalloid toxic elements widely distributed in environmental media, the geochemical behaviour of antimony (Sb) and arsenic (As) affects environmental safety. We selected the surface waters and sediments at the river-lake junction of Dongting Lake as the research objects, analysed the concentration and chemical partitioning of Sb and As, assessed its contamination and ecological risk levels, and discussed its sources and potential influencing factors. The concentrations of dissolved Sb and As in surface waters were low (< 5.46 µg/L), and the concentrations of Sb and As in surface sediments were 2.49-22.65 mg/kg and 11.10-136.34 mg/kg, respectively. Antimony and As in sediments were mainly enriched in the fraction of residues, but the proportion of As in bioavailability was significantly higher than that of Sb. Although the contamination level of Sb was higher than that of As, the risk assessment code (RAC) showed that the ecological risk level of As was higher than that of Sb. Rainwater erosion and mining activities (in the midstream of Zijiang River) were the main contaminated sources of Sb, while As was affect mainly by rainwater erosion. The contamination and ecological risk of Sb in the inlet of the Zijiang River should receive considerable attention, while those of As in the inlet of the Xiangjiang River should also be seriously considered. This study highlights the need for multi-index-based assessments of contamination and ecological risk and the importance of further studies on the environmental behaviour of metalloids in specific hydrological conditions, such as river-lake junctions.

Human activities contributing to the accumulation of high-risk trace metal(loid)s in soils of China's five major urban agglomerations

Rapid urbanization has accelerated the accumulation of trace metal(loid)s (TMs) in soils, but the relationship between this accumulation and human activities remains largely unknown. Therefore, based on 775 published literatures (2001-2020), this study aimed to identify the influence of human activities on TM accumulation. Results showed that all soil TM concentrations were higher than their corresponding Chinese soil background values. The pollution risk assessment indicated that the soil TMs in the study area were at moderate levels, and the value of Pollution load index was 2.10. According to the assessment of health risks, the non-carcinogenic risks for adults were at the "Negligible risk" level; while the carcinogenic risk was not negligible for all populations, with children being more susceptible than adults. Meanwhile, six high-risk TMs were identified based on the grading of Contaminating factors (CF ≥ 3) and contribution to health risk (≥ 75%), including four high pollution risk TMs (Cd, Hg, Cu, and Pb) and two high health risk TMs (Cr and As) . In addition, in accordance with the results of the Random forest model, the accumulation of soil high-risk TMs was closely related to influencing factors associated with human activities. The accumulation of Hg and Cr among five major urban agglomerations had the same influencing factors (the number of industrial companies and the amount of industrial wastewater discharge for Hg; the amount of pesticide application and highway mileage for Cr). However, there were significant differences in the factors influencing the accumulation of the other four high-risk TMs (including Cd, As, Cu and Pb), due to the different characteristics of each urban agglomeration. Our results provide new insights into the relationship between human activities and soil TM accumulation.

Connotation, status, and governance of land ecological security in China's new urbanization: recent advances and future prospects

The rapid development of China's new urbanization has created favorable conditions for economic growth and social development. Urbanization includes population urbanization and land urbanization, among which land urbanization leads to land ecological security problems. At present, there is a lack of comprehensive understanding of land ecological security in China's new urbanization construction. This paper aims to fill the gap by systematically combing relevant literature on the connotation, status, and governance of land ecological security in China's new urbanization. Literature review shows that China's land ecological security is still at a low level, and the new urbanization construction has significant impacts on land ecological security. Land contamination is the most critical factor threatening land ecological security, and there are differences in the levels of land contamination and types of pollutants in different new urbanization construction forms. According to an example of land ecological security governance with enterprises as the main body and multiple subjects cooperating, the governance of land ecological security needs to integrate a variety of different subjects to coordinate governance. Future research directions should focus on the construction of land ecological security assessment index system, development of land contamination multi-level control technology, and construction of multi-subject collaborative governance model with "government-enterprise-social organization-residents."

An integrated overview of metals contamination, source-specific risks investigation in coal mining vicinity soils

Heavy metals in soil are harmful to natural biodiversity and human health, and it is difficult to estimate the effects accurately. To reduce pollution and manage risk in coal-mining regions, it is essential to evaluate risks for heavy metals in soil. The present study reviews the levels of 21 metals (Nb, Zr, Ag, Ni, Na, K, Mg, Rb, Zn, Ca, Sr, As, Cr, Fe, Pb, Cd, Co, Hg, Cu, Mn and Ti) in soils around Barapukuria coal-mining vicinity, Bangladesh which were reported in literature. An integrated approach for risk assessments with the positive matrix factorization (PMF) model, source-oriented ecological and health hazards were applied for the study. The contents of Rb, Ca, Zn, Pb, As, Ti, Mn, Co, Ag, Zr, and Nb were 1.63, 1.10, 1.97, 14.12, 1.20, 3.13, 1.22, 3.05, 3.85, 5.48, and 7.21 times greater than shale value. About 37%, 67%, 12%, and 85% of sampling sites posed higher risks according to the modified contamination factor, Nemerow pollution index, Nemerow integrated risk index, and mean effect range median quotient, respectively. Five probable metal sources were computed, including industrial activities to coal mining (17%), agricultural activities (33%), atmospheric deposition (19%), traffic emission (16%), and natural sources (15%). Modified Nemerow integrated risk index reported that agricultural activities, industrial coal mining activities, and atmospheric deposition showed moderate risk. Health hazards revealed that cancer risk values computed by the PMF-HHR model with identified sources were higher than the standard value (1.0E-04) for children, adult male, and female. Agricultural activities showed higher cancer risks to adult male (39%) and children (32%) whereas traffic emission contributed to female (25%). These findings highlight the ecological and health issues connected to potential sources of metal contamination and provide useful information to policymakers on how to reduce such risks.

Monte Carlo simulation and delayed geochemical hazard revealed the contamination and risk of arsenic in natural water sources

Due to its ubiquity and carcinogenicity, the geochemical behavior and health risks of arsenic (As) have been a research focus worldwide. A comprehensive investigation was conducted on the contamination and ecological and health risks of As in the Zijiang River (ZR)-a natural water source. The concentration ranges of As were separately 1.36-6.23 μg/L, 11.42-74.53 mg/kg, and 1.26-130.68 μg/L in surface waters (dissolved), sediments, and pore waters. The concentrations of As in the midstream pore waters and sediments were relatively high, which was related to mining, dam interception, and sediment resuspension. The Monte Carlo simulation results showed that the occurrence probability of As contamination and static risk in sediments was low, however, in the midstream, the secondary risk caused by the release of As should be given more consideration. In the sediments, the transformation paths and the dynamic risk of As were explored based on the delayed geochemical hazard model, showing that there was a probability of a potential burst of 26.47% - 55.88% in the sediments of the ZR. Although at the detected surface waters, the total risk of the noncarcinogenicity and carcinogenicity of As were low, overall adults have lower health risks than children, and As exposure in children should be of concern. This study complements the further understanding of the geochemical behavior of arsenic, which can be extended to other toxic metal(loid)s.

Introducing a land use-based weight factor in regional health risk assessment of PAHs in soils of an urban agglomeration

The high heterogeneity of land uses in urban areas has led to large spatial variations in the contents and health risks of polycyclic aromatic hydrocarbons (PAHs) in soils. A land use-based health risk assessment (LUHR) model was proposed for soil pollution on a regional scale by introducing a land use-based weight factor, which considered the differences in exposure levels of soil pollutants to receptor populations between land uses. The model was applied to assess the health risk posed by soil PAHs in the rapidly industrializing urban agglomeration of Changsha-Zhuzhou-Xiangtan Urban Agglomeration (CZTUA). The mean concentration of total PAHs (∑PAHs) in CZTUA was 493.2 μg/kg, and their spatial distribution was consistent with emissions from industry and vehicles. The LUHR model suggested the 90th percentile health risk value was 4.63 × 10-7, which was 4.13 and 1.08 times higher than those of traditional risk assessments that have adopted adults and children as default risk receptors, respectively. The risk maps of LUHRs showed that the ratios of the area exceeding the risk threshold (1 × 10-6) to the total area were 34.0 %, 5.0 %, 3.8 %, 2.1 %, and 0.2 % in the industrial area, urban green space, roadside, farmland, and forestland, respectively. The LUHR model back-calculated the soil critical values (SCVs) for ∑PAHs under different land uses, resulting in values of 6719, 4566, 3224, and 2750 μg/kg for forestland, farmland, urban green space, and roadside, respectively. Compared with the traditional health risk assessment models, this LUHR model identified high-risk areas and drew risk contours more accurately and precisely by considering both the spatial variances of soil pollution and their exposure levels to different risk receptors. This provides an advanced approach to assessing the health risks of soil pollution on a regional scale.

Organophosphate esters in soils of Beijing urban parks: Occurrence, potential sources, and probabilistic health risks

Organophosphate esters (OPEs) are an emerging contaminant widely distributed in the soil. OPEs have drawn increasing attention for their biological toxicity and possible threat to human health. This research investigated the pollution characteristics of two typical OPEs, organophosphate triesters (tri-OPEs) and organophosphate diesters (di-OPEs), in soils of 104 urban parks in Beijing. The median concentrations of Σ₁₁tri-OPEs and Σ₈di-OPEs were 157 and 17.9 ng/g dw, respectively. Tris(2-chloroisopropyl) phosphate and bis(2-ethylhexyl) phosphate were the dominant tri-OPE and di-OPE, respectively. Consumer materials (such as building insulation and decorative materials), traffic emissions, and reclaimed water irrigation may be critical sources of tri-OPEs in urban park soils. Di-OPEs mainly originated from the degradation of parent compounds and industrial applications. Machine learning models were employed to determine the influencing factors of OPEs and predict changes in their concentrations. The predicted OPEs concentrations in Beijing urban park soils in 2025 and 2030 are three times and five times those in 2018, respectively. According to probabilistic health risk assessment, non-carcinogenic and carcinogenic risks of OPEs can be negligible for children and adults. Our results could inform measures for preventing and controlling OPEs pollution in urban park soils.

Soil heavy metal(loid) pollution and health risk assessment of farmlands developed on two different terrains on the Tibetan Plateau, China

The quality of farmland soils on the Tibetan Plateau is important because of the region's ecological vulnerability and their close link with local food security. Investigation on the pollution status of heavy metal (loid)s (HMs) in the farmlands of Lhasa and Nyingchi on the Tibetan Plateau, China revealed that Cu, As, Cd, Tl, and Pb were apparently enriched, with the soil parent materials being the primary sources of the soil HMs. Overall, the farmlands in Lhasa had higher contents of HMs compared to those in the farmlands of Nyingchi, which could be attributed to the fact that the former were mainly developed on river terraces while the latter were mainly developed on the alluvial fans in mountainous areas. As displayed the most apparent enrichment, with the average concentrations in the vegetable field soils and grain field soils of Lhasa being 2.5 and 2.2 times higher compared to those of Nyingchi. The soils of vegetable fields were more heavily polluted than those of grain fields, probably due to the more intensive input of agrochemicals, particularly the use of commercial organic fertilizers. The overall ecological risk of the HMs in the Tibetan farmlands was low, while Cd posed medium ecological risk. Results of health risk assessment show that ingestion of the vegetable field soils could pose elevated health risk, with children facing greater risk than adults. Among all the HMs targeted, Cd had relatively high bioavailability of up to 36.2% and 24.9% in the vegetable field soils of Lhasa and Nyingchi, respectively. Cd also showed the most significant ecological and human health risk. Thus, attention should be paid to minimize further anthropogenic input of Cd to the farmland soils on the Tibetan Plateau.

Source, environmental behavior and potential health risk of rare earth elements in Beijing urban park soils

Rare earth elements (REEs) have been increasingly diffused to the environment due to their extensive use and application in industries, agriculture, and high-tech devices, which have been regarded as emerge pollutants. However, the study concerning REEs in urban soils is still limited. Therefore, the objectives of this study were to investigate the potential source and risk of REEs in urban environment. We analyzed the concentration and distribution of REEs in urban park soils, and performed a combination of micro geochemical method and random forest method to characterize the pollution sources of REEs. The results showed that the ΣREE concentrations in Beijing urban park soils ranged from 117.19 to 198.09 mg/kg. Spatial distribution indicated that the high concentrations of REEs were mainly concentrated in the west of Beijing near an industrial area. The geochemical parameters, micro spherules and random forest results confirmed the anthropogenic pollution sources from industry and traffic. Risk assessment showed that the average daily doses of total REEs for children and adults were far below the reference threshold with values of 0.08 and 0.02 µg/kg/day, respectively. Our study has exhibited that though the reconstruction of parks from abandoned industrial sites showed an accumulation of REEs, the health risk of REEs for human beings are negligible.

Biomonitoring of polycyclic aromatic hydrocarbons, heavy metals, and plasticizers residues: role of bees and honey as bioindicators of environmental contamination

Polycyclic aromatic hydrocarbons (PAHs), heavy metals, and plasticizer residues are continuously released into the environment. The use of living organisms, such as Apis mellifera L. and honey, is advantageous as bioindicator of the environmental health status, instead of traditional monitoring methods, showing the ability to record spatial and temporal pollutant variations. The PAHs and heavy metal presence were determined in two sampling years (2017 and 2018) in five different locations in the Molise region (Italy), characterized by different pollution levels. During 2017, most PAHs in all samples were lower than limit of detection (LOD), while in 2018, their mean concentration in bee and honey samples was of 3 μg kg^-1 and 35 μg kg^-1, respectively. For heavy metals, lower values were detected in 2017 (Be, Cd, and V below LOD), while in 2018, the mean concentrations were higher, 138 μg kg^-1 and 69 μg kg^-1, in bees and honey, respectively. Honey has been used as indicator of the presence of phthalate esters and bisphenol A in the environment. The satisfactory results confirmed that both bees and honey are an important tool for environmental monitoring. The chemometric analysis highlighted the differences in terms of pollutant concentration and variability in the different areas, validating the suitability of these matrices as bioindicators.

Beryllium contamination and its risk management in terrestrial and aquatic environmental settings

Beryllium (Be) is a relatively rare element and occurs naturally in the Earth's crust, in coal, and in various minerals. Beryllium is used as an alloy with other metals in aerospace, electronics and mechanical industries. The major emission sources to the atmosphere are the combustion of coal and fossil fuels and the incineration of municipal solid waste. In soils and natural waters, the majority of Be is sorbed to soil particles and sediments. The majority of contamination occurs through atmospheric deposition of Be on aboveground plant parts. Beryllium and its compounds are toxic to humans and are grouped as carcinogens. The general public is exposed to Be through inhalation of air and the consumption of Be-contaminated food and drinking water. Immobilization of Be in soil and groundwater using organic and inorganic amendments reduces the bioavailability and mobility of Be, thereby limiting the transfer into the food chain. Mobilization of Be in soil using chelating agents facilitates their removal through soil washing and plant uptake. This review provides an overview of the current understanding of the sources, geochemistry, health hazards, remediation practices, and current regulatory mandates of Be contamination in complex environmental settings, including soil and aquatic ecosystems.

Development of an interpretable machine learning model associated with heavy metals' exposure to identify coronary heart disease among US adults via SHAP: Findings of the US NHANES from 2003 to 2018

Limited information is available on the links between heavy metals' exposure and coronary heart disease (CHD). We aim to establish an efficient and explainable machine learning (ML) model that associates heavy metals' exposure with CHD identification. Our datasets for investigating the associations between heavy metals and CHD were sourced from the US National Health and Nutrition Examination Survey (US NHANES, 2003-2018). Five ML models were established to identify CHD by heavy metals' exposure. Further, 11 discrimination characteristics were used to test the strength of the models. The optimally performing model was selected for identification. Finally, the SHapley Additive exPlanations (SHAP) tool was used for interpreting the features to visualize the selected model's decision-making capacity. In total, 12,554 participants were eligible for this study. The best performing random forest classifier (RF) based on 13 heavy metals to identify CHD was chosen (AUC: 0.827; 95%CI: 0.777-0.877; accuracy: 95.9%). SHAP values indicated that cesium (1.62), thallium (1.17), antimony (1.63), dimethylarsonic acid (0.91), barium (0.76), arsenous acid (0.79), total arsenic (0.01) in urine, and lead (3.58) and cadmium (4.66) in blood positively contributed to the model, while cobalt (-0.15), cadmium (-2.93), and uranium (-0.13) in urine negatively contributed to the model. The RF model was efficient, accurate, and robust in identifying an association between heavy metals' exposure and CHD among US NHANES 2003-2018 participants. Cesium, thallium, antimony, dimethylarsonic acid, barium, arsenous acid, and total arsenic in urine, and lead and cadmium in blood show positive relationships with CHD, while cobalt, cadmium, and uranium in urine show negative relationships with CHD.

Countrywide Spatial Variation of Potentially Toxic Element Contamination in Soils of Turkey and Assessment of Population Health Risks for Nondietary Ingestion

Countrywide surface soil concentrations of potentially toxic elements (PTEs) in Turkey were reviewed in the Web of Science database. A total of 93 papers were investigated to compose a PTE dataset for determining spatial variations and estimating exposure and health risks. Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were selected as PTEs in surface soil. A compiled PTE concentration dataset was used to estimate chronic toxic risks (CTRs) and carcinogenic risks (CRs) according to the deterministic and probabilistic approaches. While the CTR and CR levels of age and sex groups were estimated using a deterministic approach, population risks were estimated using a probabilistic approach. CTR and CR levels in lower age groups and female sex groups were estimated to be higher than those in higher age groups and associated male sex groups. The average CTR levels of the nondietary ingestion of As-containing soil in <11 year age groups were near/just above the threshold level, while As-associated average CR levels of adults and other age groups were estimated to be in the acceptable risk range (10^-6 < CR < 10^-5) and low priority risk range (10^-5 < CR < 10^-4), respectively. As-, Cr(VI)-, and Pb-associated upper-bound CR levels of the Turkish population were simulated to be 5.14 × 10^-4, 6.23 × 10^-5, and 2.34 × 10^-6, respectively. Health risk models show the significance of As in both chronic toxic and carcinogenic effects.

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives

Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.

Human health risk-based soil environmental criteria (SEC) for park soil in Beijing, China

Urban parks are important places that allow urban residents to experience nature but are also associated with the risk of exposure to contaminated soil. Therefore, it is necessary to establish appropriate soil environment criteria (SEC) to manage park soil quality. Studies on the demographic characteristics and behavioral patterns of urban park visitors are helpful for the selection of sensitive receptors and the determination of parameters in the establishment of SEC. This study explored the park visitors' demographic characteristics and behavioral patterns, and applied the results to derive SEC. Eighty-six parks in Beijing were selected, and mobile phone data were obtained to analysis the demographic characteristics and residence time of the visitors. Kruskal-Wallis test, kernel density estimation and random forest model were used for data analysis. The CLEA model was used to derive SEC. The results showed that the demographic characteristics and behavioral patterns of visitors in different types of parks were quite different. Parks were mostly used by males and visitors aged 31-45. Most visitors stayed in the park for 1-2 h, and the distance from a given visitor's home to the park was the most important factor affecting stay time. Then, several parameters such as the parameters related to the receptors and occupation period were optimized, and the SEC of sensitive parks and non-sensitive parks were derived. Exposure frequency may be the main reason for the difference of SEC between the two types of parks. The SECs of sensitive parks were higher than the soil screening values (SSVs) for class 1 land in GB36600-2018, indicating that the current SSVs for some parks may be too conservative. This study provides a reference for the formulation and revision of soil environmental standards for park land, and suggests strengthening research on human behavioral patterns.

A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity

Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R2: 0.70-0.99; p-value < 0.01). All models had a high reliability cross- validation success rates (>75%), and the HC5 predicted with the QSAR-ICE-SSD model was 2-fold than that derived with measured experimental data. The HC5 values demonstrated nearly linear decreasing trend from 2-MP to 4-HTP, while the decreasing trend from 4-HTP to 4-DP became shallower, indicates that the toxicity of APs to aquatic organisms increases with the addition of alkyl carbon chain lengths. The ecological risks assessment (ERA) of APs revealed that aquatic organisms were at risk from exposure to 4-NP at most river stations (the highest risk quotient (RQ) = 1.51), with the highest relative risk associated with 2.9% of 4-NP detected in 82.9% of the sampling sites. The targeted APs posed potential ecological risks in the Yongding and Beiyun River according to the mixture ERA. The potential application of QSAR-ICE-SSD models could satisfy the immediate needs for HC5 derivations without the need for additional in vivo testing.

Honeybees as Bioindicators of Heavy Metal Pollution in Urban and Rural Areas in the South of Italy

The honeybee (Apis mellifera L.) has been used in several studies for monitoring the environmental health status in terms of pollution, due to its wide-ranging foraging flights. Based on this consideration, this study aimed to analyze heavy metal pollution in Molise Region (Italy), by investigating five sites characterized by different levels of contamination. Furthermore, the authors carried out a sampling activity for a long period, in order to obtain a complete dataset. In this way, detailed information about the status of the environments was able to be obtained. The main purpose of this work was to assess the health status of Molise Region and to confirm the suitability of honeybees as environmental bioindicators of heavy metal pollution, by analyzing their variability over time and space. Furthermore, the study compared the health status associated with contamination in terms of heavy metals with that in two different areas of Italy, using hierarchical cluster analysis and principal component analysis, to evaluate the correlation existing among the three different areas of Italy. Following the findings, the authors suggest the use of honeybees as a bioindicator for heavy metal pollution in air quality studies.

Insight into the Adsorption Behaviors of Antimony onto Soils Using Multidisciplinary Characterization

Antimony (Sb) pollution in soils is an important environmental problem, and it is imperative to investigate the migration and transformation behavior of Sb in soils. The adsorption behaviors and interaction mechanisms of Sb in soils were studied using integrated characterization techniques and the batch equilibrium method. The results indicated that the adsorption kinetics and isotherms of Sb onto soils were well fitted by the first-order kinetic, Langmuir, and Freundlich models, respectively, while the maximum adsorbed amounts of Sb (III) in soil 1 and soil 2 were 1314.46 mg/kg and 1359.25 mg/kg, respectively, and those of Sb (V) in soil 1 and soil 2 were 415.65 mg/kg and 535.97 mg/kg, respectively. In addition, pH ranging from 4 to 10 had little effect on the adsorption behavior of Sb. Moreover, it was found that Sb was mainly present in the residue fractions, indicating that Sb had high geochemical stability in soils. SEM analysis indicated that the distribution positions of Sb were highly coincident with Ca, which was mainly due to the existence of calcium oxides, such as calcium carbonate and calcium hydroxide, that affected Sb adsorption, and further resulted in Sb and Ca bearing co-precipitation. XPS analysis revealed the valence state transformation of Sb (III) and Sb (V), suggesting that Fe/Mn oxides and reactive oxygen species (ROS) served as oxidant or reductant to promote the occurrence of the Sb redox reaction. Sb was mobile and leachable in soils and posed a significant threat to surface soils, organisms, and groundwater. This work provides a fundamental understanding of Sb adsorption onto soils, as well as a theoretical guide for studies on the adsorption and migration behavior of Sb in soils.