Health risk assessment of heavy metal(loid)s in park soils of the largest megacity in China by using Monte Carlo simulation coupled with Positive matrix factorization model

Huang, Jinbo; Wu, Yijian; Sun, Jiaxun; Xiao, Li; Geng, Xiaolei; Zhao, Menglu; Sun, Tao; Fan, Zhengqiu

doi:10.1016/j.jhazmat.2021.125629

Cited by 238 publications

(57 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The mean HQs of all population categories in descending order are as follows: As > Pb > Cr > Sb > Mn > Cd > Ni > Cu > Zn. Generally, the high CR values of As, Cd, Cr, and Pb were above the acceptable threshold value of 1E‐6 (Figure 7), implying a potential CR (Huang et al, 2021). Importantly, the mean CR of As in children (1.59E‐4) was higher than the unacceptable threshold value (1E‐4).…”

Section: Resultsmentioning

confidence: 98%

“…Based on the results of the receptor models (Table S9), the health risks by different sources, including NCR and TCR, were also investigated. Although different exposure levels in different groups, same contribution rates of different factors could be expected due to the same contribution rates to HM in soils (Huang et al, 2021). The total NCR (Figure S7) was dominated by Factor 5 (resolved as coal mining and Sb mining), followed by Factor 1 (Pb–Zn mining and smelting).…”

Section: Discussionmentioning

confidence: 99%

“…An HI or HQ >1 was indicative of a potential adverse health risk (Mohseni Bandpi et al, 2018). The summation of all the potential risks of individual CR yielded the TCR value (Huang et al, 2021). If the CR or TCR >1E‐4, it indicated a CR; if the CR or TCR <1E‐4, there is no significant CR; and if the CR or TCR <1E‐6, there was a negligible risk (USEPA, 2009).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk

et al. 2022

View full text Add to dashboard Cite

The Karst Plateau is characterized by elevated heavy metals (HM), the farmland soils in the Karst Plateau area is especially vulnerable to HM pollution. To cope with soil HM pollution and conduct precaution in Karst Plateau, the key bottleneck is to understand the pollution levels, sources, and priority‐control of HM. Hence, geochemical baselines of HM in farmland soils were established to accurately evaluate the pollution characteristics. Pollution sources were identified with multivariate statistics, geostatistical methods, and receptor models. Priority‐control of HM were distinguished via health assessments with a Monte Carlo simulation. A remarkable accumulation of Pb, Sb, Zn, As, and Cd was observed. Hotspots of As, Cd, Pb, Sb, and Zn clustered in the southwestern region of Hezhang. Pb–Zn related activities, cement product activities, coal mining, and coal combustion were dominant sources. Both noncarcinogenic risk and carcinogenic risk followed the order: children>adult females>adult males. As, Cd, and Pb were found to be priority contaminants in farmland soils in Hezhang.

show abstract

Section: Resultsmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk

et al. 2022

View full text Add to dashboard Cite

show abstract

“…S1). Consequently, the health risks associated with soil pollution in urban parks have attracted widespread attention (Huang et al, 2021;Penteado et al, 2021;Qu et al, 2020), and the Chinese government published the rst risk control standard for soil contamination of park land in 2018.…”

Section: Introductionmentioning

confidence: 99%

Using Mobile Phone Data to Understand the Demographic Characteristics and Behavioral Patterns of Park Visitors in a Megacity, Beijing, China

Zhao

Ma³

et al. 2021

Preprint

View full text Add to dashboard Cite

BackgroundUrban parks are important places that allow urban residents to experience nature but are also associated with the risk of exposure to contaminated soil. Local researches on demographic characteristics and population behavior patterns are the basis of soil exposure assessment. The objectives were to determine park visitors’ demographic characteristics and behavioral patterns. MethodsA total of 86 urban parks in Beijing were selected, and mobile phone data were used to quantify the park visits, identify the demographic characteristics of the visitors, and determine the visitors’ stay times. A Kruskal-Wallis test and kernel density estimation were used for difference analysis and to describe the spatial aggregation of visitors, respectively. A random forest model was used to analyze the factors influencing the visitors’ stay times.ResultsVisitor numbers at comprehensive parks and theme parks decreased significantly in winter, whereas seasonal variations in visitor numbers at community parks and country parks were small. Almost half of the visitors to Beijing’s urban parks were foreign visitors, who, unlike local visitors, preferred to visit parks located in the city’s center. 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, contributing 80.65% of the difference in stay time.ConclusionsThis study provides a quantitative analysis of the patterns relating to urban park use by different groups of people. Changes in behavior patterns of different groups of people should be considered when assessing soil exposure.

show abstract

“…Thus, it makes the assessment more informative to risk managers [ 6 , 7 ]. MCS is widely used in the risk estimation of multimedia exposure and parameter sensitivity qualification [ 8 , 9 , 10 ]. Recently, MCS has been combined with the physiologically based pharmacokinetic model to determine the relationship between internal exposure dose and cancer risk [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation

Qin

Tuerxunbieke

Wang

et al. 2021

IJERPH

View full text Add to dashboard Cite

Monte Carlo simulation (MCS) is a computational technique widely used in exposure and risk assessment. However, the result of traditional health risk assessment based on the MCS method has always been questioned due to the uncertainty introduced in parameter estimation and the difficulty in result validation. Herein, data from a large-scale investigation of individual polycyclic aromatic hydrocarbon (PAH) exposure was used to explore the key factors for improving the MCS method. Research participants were selected using a statistical sampling method in a typical PAH polluted city. Atmospheric PAH concentrations from 25 sampling sites in the area were detected by GC-MS and exposure parameters of participants were collected by field measurement. The incremental lifetime cancer risk (ILCR) of participants was calculated based on the measured data and considered to be the actual carcinogenic risk of the population. Predicted risks were evaluated by traditional assessment method based on MCS and three improved models including concentration-adjusted, age-stratified, and correlated-parameter-adjusted Monte Carlo methods. The goodness of fit of the models was evaluated quantitatively by comparing with the actual risk. The results showed that the average risk derived by traditional and age-stratified Monte Carlo simulation was 2.6 times higher, and the standard deviation was 3.7 times higher than the actual values. In contrast, the predicted risks of concentration- and correlated-parameter-adjusted models were in good agreement with the actual ILCR. The results of the comparison suggested that accurate simulation of exposure concentration and adjustment of correlated parameters could greatly improve the MCS. The research also reveals that the social factors related to exposure and potential relationship between variables are important issues affecting risk assessment, which require full consideration in assessment and further study in future research.

show abstract

Health risk assessment of heavy metal(loid)s in park soils of the largest megacity in China by using Monte Carlo simulation coupled with Positive matrix factorization model

Cited by 238 publications

References 63 publications

Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk

Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk

Using Mobile Phone Data to Understand the Demographic Characteristics and Behavioral Patterns of Park Visitors in a Megacity, Beijing, China

Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation

Contact Info

Product

Resources

About