Bioaccessibility of arsenic, lead, and cadmium in contaminated mining/smelting soils: Assessment, modeling, and application for soil environment criteria derivation

Xie, Kunting; Xie, Nangeng; Liao, Zhiyang; Luo, Xiao-San; Peng, Wei; Yuan, Yong

doi:10.1016/j.jhazmat.2022.130321

Cited by 21 publications

(6 citation statements)

References 62 publications

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“…The results revealed that frequent tire friction and brake wear significantly increased the exchangeable fraction of these toxic metal(loid)s, except for Pb (0.09 mg/kg). The chemical interaction of metal(loid)s (bearing particles) with soil/dust components, including clays, Fe/Mn (oxyhydr)oxides, organic matter, and silicate minerals, as well as their behavior with respect to the soil pH, resulted in their repartitioning [78,80,81]. The low sorption ability/capacity of trace metal(loid)s by dust often results in substantial mobility, and a low content of iron/aluminum oxides and organic matter may also favor trace metal(loid) immobilization [82].…”

Section: Sequential Extraction Of Trace Metal(loid)s From Dust and Ga...mentioning

confidence: 99%

Trace Metal(loid) Migration from Road Dust to Local Vegetables and Tree Tissues and the Bioaccessibility-Based Health Risk: Impacts of Vehicle Operation-Associated Emissions

Liu

Chen

Cui

et al. 2023

IJERPH

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Traffic activities release large amounts of trace metal(loid)s in urban environments. However, the impact of vehicle operation-associated emissions on trace metal(loid) enrichment in road dust and the potential migration of these trace metal(loid)s to the surrounding environment remain unclear. We evaluated the contamination, sequential fraction, and bioaccessibility of trace metal(loid)s in urban environments by assessing their presence in road dust, garden vegetables, and tree tissues, including bark and aerial roots, at a traffic-training venue impacted by vehicle operation emissions and, finally, calculated the bioaccessibility-based health risk. The results indicated a significant accumulation of trace metal(loid)s in road dust, with the highest lead (Pb), cadmium (Cd), and antimony (Sb) concentrations in the garage entrance area due to higher vehicle volumes, frequent vehicle starts and stops, and lower speeds. Aerial roots exposed to hill start conditions exhibited the highest Pb, Zn, and Sb levels, potentially caused by high road dust resuspension, confirming that this tree tissue is an appropriate bioindicator. Sequential extraction revealed high percentages of carbonate-, Fe/Mn oxide-, and organic/sulphide-associated fractions of Pb, copper (Cu), and zinc (Zn) in road dust, while most Cd, Cr, Ni, and Sb occurred as residual fractions. According to the potential mobilizable fractions in sequential extraction, the in vitro gastrointestinal method could be more suitable than the physiologically based extraction test to evaluate the bioaccessibility-related risk of traffic-impacted road dust. The bioaccessibility-based health risk assessment of the road dust or soil confirmed no concern about noncarcinogenic risk, while the major risk originated from Pb although leaded gasoline was prohibited before the venue establishment. Furthermore, the cancer risks (CRs) analysis showed the probable occurrence of carcinogenic health effects from Cd and Ni to adults and from Cd, Cr, and Ni to children. Furthermore, the Cd and Pb concentrations in the edible leaves of cabbage and radish growing in gardens were higher than the recommended maximum value. This study focused on the health risks of road dust directly impacted by vehicle emissions and provides accurate predictions of trace metal(loid) contamination sources in the urban environment.

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Section: Sequential Extraction Of Trace Metal(loid)s From Dust and Ga...mentioning

confidence: 99%

Trace Metal(loid) Migration from Road Dust to Local Vegetables and Tree Tissues and the Bioaccessibility-Based Health Risk: Impacts of Vehicle Operation-Associated Emissions

Liu

Chen

Cui

et al. 2023

IJERPH

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“…Interestingly, the ratio of cadmium concentration in the root to the soil (bioaccumulation coefficient) More than 1, centella Asiatica may be a potential source of cadmium bioaccumulation (Kunting et al, 2022).…”

Section: Relevance Of Researchmentioning

confidence: 99%

“…Therefore, it is imperative to assess the bioavailability of PHEs in the soil of mining/smelting sites in order to determine soil standards for mining and metallurgy with regional characteristics (Kunting et al, 2022).…”

Section: Relevance Of Researchmentioning

confidence: 99%

Assessment of Environmental Damage of Non–ferrous Metallurgy Waste to the Environment

Daribayev,

Kutzhanova,

Issayev

et al. 2023

SGTS

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Бас редактор ЖҰРЫНОВ Мұрат Жұрынұлы, химия ғылымдарының докторы, профессор, ҚР ҰҒА академигі, Қазақстан Республикасы Ұлттық Ғылым академиясының президенті, АҚ «Д.В. Сокольский атындағы отын, катализ және электрохимия институтының» бас директоры (Алматы, Қазақстан) H = 4 Р е д а к ц и я л ы қ а л қ а: АБСАДЫКОВ Бахыт Нарикбайұлы, техника ғылымдарының докторы, профессор, ҚР ҰҒА жауапты хатшысы, А.Б. Бектұров атындағы химия ғылымдары институты (Алматы, Қазақстан) H = 5 ӘБСАМЕТОВ Мәліс Құдысұлы (бас редактордың орынбасары), геология-минералогия ғылымдарының докторы, профессор, ҚР ҰҒА академигі, «У.М. Ахмедсафина атындағы гидрогеология және геоэкология институтының» директоры (Алматы, Қазақстан) H = 2 ЖОЛТАЕВ Герой Жолтайұлы (бас редактордың орынбасары), геология-минералогия ғылымдарының докторы, профессор, Қ.И. Сатпаев тындағы геология ғылымдары институтының директоры (Алматы, Қазақстан) Н=2 СНОУ Дэниел, Рһ.D, қауымдастырылған профессор, Небраска университетінің Су ғылымдары зертханасының директоры (Небраска штаты, АҚШ) H = 32 ЗЕЛЬТМАН Реймар, Рһ.D, табиғи тарих мұражайының Жер туралы ғылымдар бөлімінде петрология және пайдалы қазбалар кен орындары саласындағы зерттеулердің жетекшісі (Лондон, Англия) H = 37 ПАНФИЛОВ Михаил Борисович, техника ғылымдарының докторы, Нанси университетінің профессоры (Нанси, Франция) Н=15 ШЕН Пин, Рһ.D, Қытай геологиялық қоғамының тау геологиясы комитеті директорының орынбасары, Американдық экономикалық геологтар қауымдастығының мүшесі (Пекин, Қытай) H = 25 ФИШЕР Аксель, Ph.D, Дрезден техникалық университетінің қауымдастырылған профессоры (Дрезден, Берлин) Н = 6 КОНТОРОВИЧ Алексей Эмильевич, геология-минералогия ғылымдарының докторы, профессор, РҒА академигі, А.А. Трофимука атындағы мұнай-газ геологиясы және геофизика институты (Новосибирск, Ресей) H = 19 АГАБЕКОВ Владимир Енокович, химия ғылымдарының докторы, Беларусь ҰҒА академигі, Жаңа материалдар химиясы институтының құрметті директоры (Минск, Беларусь) H = 13 КАТАЛИН Стефан, Рһ.D, Дрезден техникалық университетінің қауымдастырылған профессоры (Дрезден, Берлин) H = 20 СЕЙТМҰРАТОВА Элеонора Юсуповна, геология-минералогия ғылымдарының докторы, профессор, ҚР ҰҒА корреспондент-мүшесі, Қ.И. Сатпаев атындағы Геология ғылымдары институты зертханасының меңгерушісі (Алматы, Қазақстан) Н=11 САҒЫНТАЕВ Жанай, Ph.D, қауымдастырылған профессор, Назарбаев университеті (Нұр-Сұлтан, Қазақстан) H = 11 ФРАТТИНИ Паоло, Рһ.D, Бикокк Милан университеті қауымдастырылған профессоры (Милан, Италия) H = 28 «Известия НАН РК. Серия геологии и технических наук».

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“…For instance, Xie et al utilized a random forest (RF) model to predict the bioavailability of heavy metals in soil using samples from 12 metallurgical sites. 29 In contrast, Zhang et al employed conditional inference trees (CIT) and RF models to explore the primary factors influencing heavy metal bioavailability in paddy soils in the karst regions of the northern part of Guangxi, China. 30 However, due to limitations in the data, a lack of diversity in soil types, and instability in model performance, the applicability of the models developed in these studies is limited.…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers have already introduced machine learning methods to predict the bioaccessibility of PHE. For instance, Xie et al utilized a random forest (RF) model to predict the bioavailability of heavy metals in soil using samples from 12 metallurgical sites . In contrast, Zhang et al employed conditional inference trees (CIT) and RF models to explore the primary factors influencing heavy metal bioavailability in paddy soils in the karst regions of the northern part of Guangxi, China .…”

Section: Introductionmentioning

confidence: 99%

Predicting the Bioaccessibility of Soil Cd, Pb, and As with Advanced Machine Learning for Continental-Scale Soil Environmental Criteria Determination in China

Xie,

Ou,

et al. 2024

Environ. Health

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Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with soil. Traditional batch experimental methods and linear models, however, are time-consuming and often fall short in precisely quantifying bioaccessibility. In this study, using 937 data points gathered from 56 journal articles, we developed machine learning models for three harmful inorganic elements, namely, Cd, Pb, and As. After thorough analysis, the model optimized through a boosting ensemble strategy demonstrated the best performance, with an average R 2 of 0.95 and an RMSE of 0.25. We further employed SHAP values in conjunction with quantitative analysis to identify the key features that influence bioaccessibility. By utilizing the developed integrated models, we carried out predictions for 3002 data points across China, clarifying the bioaccessibility of cadmium (Cd), lead (Pb), and arsenic (As) in the soils of various sites and constructed a comprehensive spatial distribution map of China using the inverse distance weighting (IDW) interpolation method. Based on these findings, we further derived the soil environmental standards for metallurgical sites in China. Our observations from the collected data indicate a reduction in the number of sites exceeding the standard levels for Cd, Pb, and As in mining/smelting sites from 5, 58, and 14 to 1, 24, and 7, respectively. This research offers a precise and scientific approach for cross-regional risk assessment at the continental scale and lays a solid foundation for soil environmental management.

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Bioaccessibility of arsenic, lead, and cadmium in contaminated mining/smelting soils: Assessment, modeling, and application for soil environment criteria derivation

Cited by 21 publications

References 62 publications

Trace Metal(loid) Migration from Road Dust to Local Vegetables and Tree Tissues and the Bioaccessibility-Based Health Risk: Impacts of Vehicle Operation-Associated Emissions

Trace Metal(loid) Migration from Road Dust to Local Vegetables and Tree Tissues and the Bioaccessibility-Based Health Risk: Impacts of Vehicle Operation-Associated Emissions

Assessment of Environmental Damage of Non–ferrous Metallurgy Waste to the Environment

Predicting the Bioaccessibility of Soil Cd, Pb, and As with Advanced Machine Learning for Continental-Scale Soil Environmental Criteria Determination in China

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