Dataset Information

A New Analytic Model to Identify Lead Pollution Sources in Soil Based on Lead Fingerprint.

ABSTRACT: Gobeil's model is one of the most widely used models to identify lead (Pb) pollution sources in the environment. It is based on a set of equations involving Pb isotope fractions. Although a well-established numerical method, Gobeil's model is often unable to provide an accurate estimation of each pollution sources' contribution. This paper comprehensively examines the drawbacks of Gobeil's model based on a numerical analysis and proposes a revised numerical method that provides a more accurate estimation of Pb pollution sources. Briefly, the mathematical inaccuracy of Gobeil's model mainly lies in the misinterpretation of "lead fingerprint ratio balance." To address this problem, the new analytic model relies on the mass balance of total lead in the contaminated sites, and uses a set of linear equations to obtain the contribution of each pollution source based on the lead fingerprint. A subsequent case study from an industrial park in Guanzhong area of Shaanxi Province in China shows that we can calculate the lead contribution rates accurately with the new model.

SUBMITTER: Feng T

PROVIDER: S-EPMC6950129 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A New Analytic Model to Identify Lead Pollution Sources in Soil Based on Lead Fingerprint.

Feng Tao T Wang Cheng-Jun CJ Liu Yong Y Chen Meng M Fan Miao-Miao MM Li Zhi Z

International journal of environmental research and public health 20191211 24

Gobeil's model is one of the most widely used models to identify lead (Pb) pollution sources in the environment. It is based on a set of equations involving Pb isotope fractions. Although a well-established numerical method, Gobeil's model is often unable to provide an accurate estimation of each pollution sources' contribution. This paper comprehensively examines the drawbacks of Gobeil's model based on a numerical analysis and proposes a revised numerical method that provides a more accurate e ...[more]

PMID: 31835871

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Project description:BackgroundSystemic sclerosis (SSc) is a rare connective tissue disease associated with rapid evolving interstitial lung disease (SSc-ILD), driving its mortality. Specific biomarkers associated with the evolution of the lung disease are highly needed. We aimed to identify specific biomarkers of SSc-ILD to predict the evolution of the disease. Nucleosomes are stable DNA/protein complexes that are shed into the blood stream making them ideal candidates for biomarkers.MethodsWe studied circulating cell-free nucleosomes (cf-nucleosomes) in SSc patients, 31 with ILD (SSc-ILD) and 67 without ILD. We analyzed plasma levels for cf-nucleosomes and investigated whether global circulating nucleosome levels in association with or without other biomarkers of interest for systemic sclerosis or lung fibrosis (e.g., serum growth factors: IGFBP-1 and the MMP enzyme: MMP-9), could be suitable potential biomarkers for the correct identification of SSc-ILD disease.ResultsWe found that H3.1 nucleosome levels were significantly higher in patients with SSc-ILD compared SSc patients without ILD (p < 0.05) and levels of MMP-9 were significantly increased in patients with SSc-ILD compared to SSc patients without ILD (p < 0.05). Conversely, IGFBP-1 was significantly reduced in patients with SSc-ILD compared to SSc without ILD (p < 0.001). The combination of cf-nucleosomes H3.1 coupled to MMP-9 and IGFBP-1 increased the sensitivity for the differential detection of SSc-ILD. High levels of accuracy were reached with this combined model: its performances are strong with 68.4% of positive predictive value and 77.2% of negative predictive value for 90% of specificity. With our model, we identified a significant negative correlation with FVC % pred (r = -0.22) and TLC % pred (r = -0.31). The value of our model at T1 (baseline) has a predictive power over the Rodnan score at T2 (after 6-18 months), showed by a significant linear regression with R2 = 19% (p = 0.013). We identified in the sole group of SSc-ILD patients a significant linear regression with a R2 = 54.4% with the variation of DLCO between T1 and T2 (p < 0.05).ConclusionIn our study, we identified a new blood-based model with nucleosomic biomarker in order to diagnose SSc-ILD in a SSc cohort. This model is correlated with TLC and FVC at baseline and predictive of the skin evolution and the DLCO. Further longitudinal exploration studies should be performed in order to evaluate the potential of such diagnostic and predictive model.

Project description:Exposure to arsenic (As) or lead (Pb) has been associated with adverse health outcomes, and high-risk populations can be disproportionately exposed to these metals in soils. The objectives of this study were: to examine if predicted soil As and Pb concentrations at maternal residences of South Carolina (SC) low-income mothers differed based on maternal race (non-Hispanic black versus white), to examine whether differences in predicted residential soil As and Pb concentrations among black and white mothers differed by socioeconomic status (SES), and to examine whether such disparities persisted after controlling for anthropogenic sources of these metals, including direction from, and distance to industrial facilities. Kriged soil As and Pb concentrations were estimated at maternal residences in 11 locations in SC, and models with maternal race and individual and US Census block group level SES measures were examined. US Environmental Protection Agency Toxics Release Inventory (TRI) facility As and Pb releases categorized by distance and direction to block groups in which mothers resided were also identified, as were proxy measures for historic use of leaded gasoline (road density) and Pb-based paint (categories of median year home built by US Census block group). Consistent racial disparities were observed for predicted residential soil As and Pb concentrations, and the disparity was stronger for Pb than As (betas from adjusted models for black mothers were 0.12 and 2.2 for As and Pb, respectively, all p<0.006). Higher road density and older homes in block groups were more closely associated with higher predicted soil As and Pb concentrations than on-site releases of As and Pb categorized by facility location. These findings suggest that non-Hispanic black mothers in this study population had elevated residential As and Pb soil concentrations, after adjusting for SES, and that soil As and Pb concentrations were not associated with recent industrial releases.

Dataset Information

A New Analytic Model to Identify Lead Pollution Sources in Soil Based on Lead Fingerprint.

Publications

A New Analytic Model to Identify Lead Pollution Sources in Soil Based on Lead Fingerprint.

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