Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system

Men, Cong; Liu, Ruimin; Wang, Qingrui; Guo, Lihui; Miao, Yuexi; Shen, Zhenyao

doi:10.1016/j.scitotenv.2018.10.212

Cited by 101 publications

(21 citation statements)

References 94 publications

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“…The r 2 values for Cu was only 0.129 when all the data were included; however, this analysis was strongly influenced by a single outlier at site 18 (5092.5 mg kg −1 and 27 ×average Cu concentration). Following removal of this outlier, the r 2 values increased to 0.961, indicating a significant and disproportional influence of the outlier to PMF prediction results (Men et al, 2019). Similarly, spatial analysis indices, such as Moran's I is sensitive to outliers, but is strongly indicative of contaminated locations (Zhao et al, 2014(Zhao et al, , 2019.…”

Section: Assessment Of Metal Sources By Pmf Modelmentioning

confidence: 96%

A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed

Xia

Zhang

et al. 2020

Journal of Hazardous Materials

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Quantitative assessment of metal sources in sediments is essential for implementation of source control and remediation strategies. This study investigated metal contamination in sediments to assess potential ecological risks and quantify pollutant sources of metals (Cu, Zn, Pb, Cd, Cr, Co and Ni) in the Wen-Rui Tang River watershed. Total and fraction analysis indicated high pollution levels of metals. Zinc and Cd posed high ecological risk based on the risk assessment code, with the highest ecological risk found in the southwestern of the watershed. The positive matrix factorization (PMF) model was highly effective in predicting total metal concentrations and identified three contributing metal sources. An agricultural source (factor 1) contributed highly to Cu (74.1%) and Zn (42.5%), and was most prominent in the west and south-central portions of the watershed. Cd (93.5%) showed a high weighting with industrial sources (factor 2) with a hot spot in the southwest. Factor 3 was identified as a mixed natural and vehicle traffic source that showed large contribution to Cr (65.2%), Ni (63.9%) and Pb (50.7%). Spatial analysis indicated a consistent pattern between PMF-identified factors and suspected metal sources at the watershed scale demonstrating the efficacy of the PMF modeling approach for watershed analysis.

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Section: Assessment Of Metal Sources By Pmf Modelmentioning

confidence: 96%

A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed

Xia

Zhang

et al. 2020

Journal of Hazardous Materials

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“…The use of mercury-containing fertilizers and pesticides is one reason for the general increase in the mercury content in soil, water, and dust (Men et al 2018b). Hg presented in fertilizers and pesticides could volatilize into the air, transport over a certain distance, and eventually sink to the ground (Dong et al 2017;Men et al 2019;Naderizadeh et al 2016), which accelerates the Hg contamination in road dust. In addition, some medical equipment in hospitals and clinics, such as thermometers, sphygmomanometers, and amalgams utilized in dentistry and cinnabar, contain mercury (hydrargyrum), which will cause certain mercury pollution (Giersz et al 2017;Li et al 2016b).…”

Section: Identification Of Pollution Sourcesmentioning

confidence: 99%

Spatial distribution of pollution characteristics and human health risk assessment of exposure to heavy elements in road dust from different functional areas of Zhengzhou, China

Wang

Shen

Kang

et al. 2020

Environ Sci Pollut Res

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Road dust from different sources directly contacts the human body and has potential effects on public health. In this study, a total number of 87 road dust samples were collected at 29 sampling sites from five different functional areas (commercial area (CA), residential area (RA), educational area (EA), industrial area (IA), and park area (PA)) in Zhengzhou to study the contamination status, distribution, source identification, ecological risk assessment, and spatial distribution of human health risks due to eight heavy elements. The geo-accumulation index (I geo) and pollution index (PI) revealed that there was very high contamination with Cd and Hg caused by atmospheric deposition, which should be paid special attention. Additionally, the source identification indicated that Cr, Ni, Cu, Zn, Cd, and Pb originate from anthropogenic activities related to traffic, and Hg can originate from medical equipment and agricultural chemicals, while the extremely low level of pollution with As could be explained by geographic sources. Moreover, the calculated ecological risk index values were increased in the order of CA > RA > EA > IA > PA in different functional areas. According to the human health risks of the whole city, children exposed to Pb have the highest health risk, especially for CA and IA, as calculated by the noncarcinogenic hazard index (HI). For adults and children, health risks caused by Cu, Zn, and Pb were higher in the CA, RA, and PA of the downtown area, whereas Cr and Ni had the highest noncarcinogenic exposure risk in northwestern Zhengzhou due to point source pollution. Calculations of the carcinogenic risk (CR) values for Cr, Ni, As, and Cd indicate that the value of Cr is highest (1.17 × 10 −7), especially inside the industrial area (8.55 × 10 −7), which is close to the lower limit of the threshold values (10 −6 to 10 −4). These results can provide a theoretical basis and data support for air treatment, pollution control, and the implementation of public prevention in different functional areas of Zhengzhou.

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“…The PMF model, which was produced by Paatero and Tapper (1993) [ 29 ], was adopted to the source the apportionment and uncertainty analysis. In order to avoid negative values in the matrix factorization process, the PMF makes non-negative constraints on factor loading and the factor score in solving the process which makes the source component spectrum and source contribution interpretable and with clear physical significance [ 30 , 31 ]. The PMF model can be represented by the following equation:

where

is the concentration of element

in sample

is the contribution of source

on sample

;

is the contribution of source

on element

; and

is the modeling error on the concentration of element

in sample

.…”

Section: Methodsmentioning

confidence: 99%

Risk Assessment and Source Apportionment of Soil Heavy Metals under Different Land Use in a Typical Estuary Alluvial Island

Sun

Huang

et al. 2020

IJERPH

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Understanding the environmental risks of soil heavy metals (HMs) and identifying their main sources are the essential prerequisites for the prevention and management of soil pollution. Based on a detailed survey of soil HMs (Cu, Cr, Ni, Zn, Pb, Cd, As and Hg) from different land use types (including agricultural land, construction land, wetland, and forest land) in an estuary alluvial island, the environmental risk and source apportionment of soil HMs were investigated. Altogether, 117 soil samples were taken in the study area to appraise the soil HMs environmental risk by using the geo-accumulation index (Igeo), potential ecological risk index (RI), and human health risk assessment (HRA) and to identify its main sources by using positive matrix factorization (PMF) model. The average concentrations of soil HMs (except As) surpassed their reference background values in China. There were no significant differenced in the mean concentrations of HMs in different land use types, except that the Hg concentration in the construction land was significantly higher than that in others. The results of Igeo showed that Cd pollution was unpolluted to moderately polluted, and that the others were unpolluted. The potential ecological risk level for Cd and Hg was “moderated potential risk”, while for Cu, Cr, Ni, Zn, Pb and As was “low potential risk”. Higher contamination was distributed at the west-central area. The results of the HRA indicated that the non-carcinogenic risk and the carcinogenic risk that human beings suffered from HMs in different land uses were insignificant. To more accurately identify the sources of soil HMs, the PMF model coupled with the GIS-spatial analysis was applied. The results showed that agricultural activities, natural source, industrial discharge and river transportation, and atmosphere deposition were the main determining factors for the accumulation of soil HMs in the study area, with the contribution rate of 24.25%, 23.79%, 23.84%, and 28.12%, respectively. The study provides an underlying insight needed to control of the soil HM pollutions for an estuary alluvial island.

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Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system

Cited by 101 publications

References 94 publications

A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed

A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed

Spatial distribution of pollution characteristics and human health risk assessment of exposure to heavy elements in road dust from different functional areas of Zhengzhou, China

Risk Assessment and Source Apportionment of Soil Heavy Metals under Different Land Use in a Typical Estuary Alluvial Island

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