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

Bibliographic Data

ID15475299
AuthorsTao Feng (0009-0009-7262-1432, Xi'an University of Architecture and Technology, corresponding author), Chengjun Wang (0000-0001-8433-3512, Xi'an University of Architecture and Technology), Cheng-Jun Wang (0000-0002-9507-2888, School of Management, Xi’an University of Architecture & Technology, Xi’an 710055, China), Yong Liu (0000-0001-6741-4863, Xi'an University of Architecture and Technology), Meng Chen (0000-0003-4503-5226, Tsinghua University), Miaomiao Fan (0009-0004-6121-9732, Beijing University of Technology), Miao-miao Fan (Beijing University of Technology, Chaoyang, Beijing 100124, China), Zhi Li (0000-0002-6039-1045, California State University, San Bernardino)
Year2019
Volume16
Issue24
Pages5059-5059
Publication date2019-12-11
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph16245059
PMID31835871
OpenAlexW2995620009
LanguageEN
References cited31

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

Fingerprint (computing · Lead (geology · Material balance · Process engineering · Set (abstract data type · Computer Science · Engineering · Environmental Science · Heavy Metal Exposure and Toxicity · Heavy metals in environment · Toxic Organic Pollutants Impact · Artificial Intelligence · Geology · Pollution

  • Lead Poisoning

    Herbert L Needleman, Herbert Needleman•Annual Review of Medicine•2004

Citation velocityhistorical
Highly citedNo

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