ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Research Articles

Source apportionment of heavy metals in soil of Guangzhou: Comparison of three receptor models

  • 1.

    Department of Chemistry, University of Science and Technology of China, Hefei 230026, China

  • 2.

    Guangdong Ecological Environmental Monitoring Center, Guangzhou 510308, China

  • 3.

    Analysis and Testing Center of West Anhui University, Lu’ an 237012, China

  • 4.

    Medical Device Research and Testing Center, South China University of Technology, Guangzhou 510006, China

Cite this:
https://doi.org/10.52396/JUST-2021-0169
  • Received Date: 20 July 2021
  • Rev Recd Date: 31 October 2021
  • Publish Date: 30 November 2021
    Abstract

  • Abstract

    Receptor models are useful tools to identify the types of pollution source and estimate the contributions of each source of the observed samples. To analyze the concentrations, distributions and sources of eight heavy metals including lead (Pb), cadmium (Cd), zinc (Zn), mercury (Hg), arsenic (As), copper (Cu), chromium (Cr), and nickel (Ni) in soils, 208 topsoil samples were collected in the main urban area of Guangzhou, China. Three receptor models (Multi-Linear Regression of the Absolute Principal Component Scores (APCS-MLR) method, Positive Matrix Factorization (PMF) method and UNMIX method) were employed to identify the potential pollution sources of heavy metals and to apportion the pollution sources. Results show that the mean concentrations of eight heavy metal elements are higher than the corresponding background values, with the mean concentration of Cd being almost five times its background value. The three receptor models all identify three potential pollution sources, which are nature source, traffic source and industry source. Moreover, PMF and UNMIX can identify an agricultural source besides the three pollution sources, which better distinguishes the different types of pollution sources. Comparison among the results of APCS-MLR, PMF and UNMIX shows that there are some significant differences in the estimated contributions for each potential pollution source. It is also found that PMF appears to be more plausible for this investigation. It is advisable to use multiple receptor models to perform source identification and source apportionment, and the results could be very useful to local administrations for the control and management of pollution and better protection of important soil quality.

    Abstract

    Receptor models are useful tools to identify the types of pollution source and estimate the contributions of each source of the observed samples. To analyze the concentrations, distributions and sources of eight heavy metals including lead (Pb), cadmium (Cd), zinc (Zn), mercury (Hg), arsenic (As), copper (Cu), chromium (Cr), and nickel (Ni) in soils, 208 topsoil samples were collected in the main urban area of Guangzhou, China. Three receptor models (Multi-Linear Regression of the Absolute Principal Component Scores (APCS-MLR) method, Positive Matrix Factorization (PMF) method and UNMIX method) were employed to identify the potential pollution sources of heavy metals and to apportion the pollution sources. Results show that the mean concentrations of eight heavy metal elements are higher than the corresponding background values, with the mean concentration of Cd being almost five times its background value. The three receptor models all identify three potential pollution sources, which are nature source, traffic source and industry source. Moreover, PMF and UNMIX can identify an agricultural source besides the three pollution sources, which better distinguishes the different types of pollution sources. Comparison among the results of APCS-MLR, PMF and UNMIX shows that there are some significant differences in the estimated contributions for each potential pollution source. It is also found that PMF appears to be more plausible for this investigation. It is advisable to use multiple receptor models to perform source identification and source apportionment, and the results could be very useful to local administrations for the control and management of pollution and better protection of important soil quality.
    • FullText(HTML)
  • loading
    • References(39)
  • [1]
    Ren S C. Spatial variability and source analysis of heavy metal pollution in vegetable fields in urban-rural ecotone. Hangzhou: Zhejiang University, 2013.
    [2]
    Park E J, Kim D S, Park K. Monitoring of ambient particles and heavy metals in a residential area of Seoul, Korea. Environmental Monitoring and Assessment, 2008, 137: 441-449.
    [3]
    Gordon G E.Receptor models. Environmental Science and Technology, 1980, 14(7): 792-799.
    [4]
    Henry R C, Lewis C W, Hopke P K. Review of receptor model fundamentals. Atmospheric Environment, 1984, 18(8): 1507-1515.
    [5]
    Bullock K R, Duvall R M, Norris G A,et al. Evaluation of the CMB and PMF methods using organic molecular markers in fine particulate matter collected during the Pittsburgh Air Quality Study. Atmospheric Environment, 2008, 42(29): 6897-6904.
    [6]
    Borovec Z. Evaluation of the concentrations of trace elements in stream sediments by factor and cluster analysis and the sequential extraction procedure. Science of the Total Environment, 1996, 177(1): 237–250.
    [7]
    Huang F, Wang X Q, Lou L P,et al. Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Research, 2010, 44(5): 1562–1572.
    [8]
    Chueinta W, Hopke P K, Paatero P. Investigation of sources of atmospheric aerosol at urban and suburban residential areas in Thailand by positive matrix factorization. Atmospheric Environment, 2000, 34(20): 3319-3329.
    [9]
    Brown S G,Ebrly S, Paatero P, et al. Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results. Science of the Total Environment, 2015, 518: 626-635.
    [10]
    Lu X, Hu W Y, Huang B, et al. Analysis of heavy metal sources in farmland soils around mining area based on Unmix method. Environmental Science, 2018, 39(3): 1421-1429.
    [11]
    Tian F L, Chen J W, Liu C Y, et al. Application of Monte Carlo Uncertainty Analysis in the Source Analysis of Receptor models. Chinese Science Bulletin, 2011, 56(32): 2675-2680.
    [12]
    Khairy M A, Lohmann R. Source apportionment and risk assessment of polycyclic aromatic hydrocarbons in the atmospheric environment of Alexandria, Egypt. Chemosphere, 2013, 91(7): 895-903.
    [13]
    Yang B, Zhou L,Xue N, et al. Source apportionment of polycyclic aromatic hydrocarbons in soils of Huanghuai Plain, China: Comparison of three receptor models. Science of the Total Environment, 2013, 443: 31-39.
    [14]
    Chen W, Wu X, Zhang H, et al. Contamination characteristics and source apportionment of methylated PAHs in agricultural soils from Yangtze River Delta, China. Environmental Pollution, 2017, 230: 927-935.
    [15]
    Dong B, Zhang R Z, Gan Y D, et al. Multiple methods for the identification of heavy metal sources in cropland soils from a resource-based region. Science of the Total Environment, 2019, 651(2): 3127–3138.
    [16]
    Chen H Y, Teng Y G, Li J, et al. Source apportionment of trace metals in river sediments: A comparison of three methods. Environmental Pollution, 2016, 211:28-37.
    [17]
    Thurston G D, Spengler J D. A qualitative assessment of source contribution to inhalable particulate matter pollution in metropolitan Boston. Atmospheric Environment, 1985, 18: 1347-1355.
    [18]
    Paatero P, Tapper U. Positive matrix factorization: A non-negative factor method with optimal utilization of error estimates of data values. Environmetrics, 1994, 5(2): 111-126.
    [19]
    Tan J, Duan J, Ma Y, et al. Long-term trends of chemical characteristics and sources of fine particle in Foshan city, Pearl River Delta: 2008-2014. Science of the Total Environment, 2016, 565:519-528.
    [20]
    Vaccaro S, Sobiecka E, Contini S, et al. The application of positive matrix factorization in the analysis, characterization and detection of contaminated soils. Chemosphere, 2007, 69: 1055-1063.
    [21]
    Henry R C. Multivariate receptor modeling by N-dimensional edge detection. Chemometrics and Intelligent Laboratory Systems, 2003, 65: 179-189.
    [22]
    Lang Y H, Yang X, Wang H, et al. Diagnostic ratios and positive matrix factorization to identify potential sources of PAHs in sediments of the Rizhao offshore, China. Polycyclic Aromatic Compounds, 2013, 33: 161-172.
    [23]
    China National Environmental Monitoring Center (CNEMC). The Background Centrations of Soil Elements of China. Beijing: China Environmental Science Press, 1990.
    [24]
    Chen J J, Zhang H H, Liu J M, et al. Spatial distribution characteristics and influencing factors of heavy metal elements in soil surface under regional geological background in Guangdong Province. Journal of Ecology and Environment, 2011, 20(4): 646-651.
    [25]
    Tong Z Q, Gu L, Duan H J, et al. Spatial distribution of heavy metal concentrations in roadside soil based on Kriging interpolation: A case study of Zhengzhou-Kaifeng section of National Highway 310. Journal of Environmental Sciences, 2012, 32(12): 3030-3038.
    [26]
    Lin C J, Pehkonen S O. The chemistry of atmospheric mercury: A review. Atmospheric Environmental, 1999, 33: 2067-2079.
    [27]
    Kemp K. Trends and sources for heavy metals in urban atmosphere. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 2002, 189: 227-232.
    [28]
    Faiz Y, Tufail M, Javed M T, et al. Road dust pollution of Cd, Cu, Ni, Pb and Zn along Islamabad expressway, Pakistan. Microchemical Journal, 2009, 92(2): 186-192.
    [29]
    Cao Y Z, Liu X J, Xie Y F, et al. Analysis of composition and concentrations characteristics of polycyclic aromatic hydrocarbons in topsoil of main areas in China. Journal of Environmental Sciences, 2012, 32(1): 197-203.
    [30]
    Tian H Z, Wang Y, Xue Z G, et al. Atmospheric emissions estimation of Hg, As, and Se from coal-fired power plants in China,2007. Science of the Total Environment, 2011, 409:3078-3081.
    [31]
    Chen D Q, Xie Z Y, Zhang Y J, et al. Source apportionment of soil heavy metals in Guangzhou based on the PCA/APCS method and geostatistics. Ecology and Environmental Sciences, 2016, 25(6): 1014-1022.
    [32]
    Yuan Z W, Luo T, Liu X W, et al. Tracing anthropogenic cadmium emissions: From source to pollution. Science of the Total Environment, 2019, 676: 87-96.
    [33]
    Wei B, Yang L. A review of heavy metal contaminations in urban soils, urban road dusts and agricultural soils from China. Microchemical Journal, 2010, 94(2): 99-107.
    [34]
    Hu W Y, Wang H F, Dong L R, et al. Source identification of heavy metals in peri-urban agricultural soils of southeast China: An integrated approach. Environmental Pollution, 2018, 237: 650 - 661.
    [35]
    Bhattacharya P, Chesselet R, Stollenwerk K G, et al. Arsenic in the environment: Biology and chemistry. Science of the Total Environment, 2007, 379: 109-120.
    [36]
    Zheng Y M, Luo J F, Chen T B, et al. Characteristics of cadmium concentrations in soils of different land use types in Beijing. Geographical Research, 2005, 24(4): 542-548.
    [37]
    Markus J, Mcbratney A. A review of the contamination of soil with lead II. Spatial distribution and risk assessment of soil lead. Environment International, 2001, 27(5): 399-411.
    [38]
    Wang P L. Environmental geochemical characteristics of near-surface atmospheric dust in Chengdu. Chengdu: Chengdu University of Technology, 2004.
    [39]
    Gholizadeh M H, Melesse A M, Reddi L. Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida. Science of The Total Environment, 2016, 566–567: 1552-1567.
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    |
    Get Citation
    PDF
    XML
    [1]
    Ren S C. Spatial variability and source analysis of heavy metal pollution in vegetable fields in urban-rural ecotone. Hangzhou: Zhejiang University, 2013.
    [2]
    Park E J, Kim D S, Park K. Monitoring of ambient particles and heavy metals in a residential area of Seoul, Korea. Environmental Monitoring and Assessment, 2008, 137: 441-449.
    [3]
    Gordon G E.Receptor models. Environmental Science and Technology, 1980, 14(7): 792-799.
    [4]
    Henry R C, Lewis C W, Hopke P K. Review of receptor model fundamentals. Atmospheric Environment, 1984, 18(8): 1507-1515.
    [5]
    Bullock K R, Duvall R M, Norris G A,et al. Evaluation of the CMB and PMF methods using organic molecular markers in fine particulate matter collected during the Pittsburgh Air Quality Study. Atmospheric Environment, 2008, 42(29): 6897-6904.
    [6]
    Borovec Z. Evaluation of the concentrations of trace elements in stream sediments by factor and cluster analysis and the sequential extraction procedure. Science of the Total Environment, 1996, 177(1): 237–250.
    [7]
    Huang F, Wang X Q, Lou L P,et al. Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Research, 2010, 44(5): 1562–1572.
    [8]
    Chueinta W, Hopke P K, Paatero P. Investigation of sources of atmospheric aerosol at urban and suburban residential areas in Thailand by positive matrix factorization. Atmospheric Environment, 2000, 34(20): 3319-3329.
    [9]
    Brown S G,Ebrly S, Paatero P, et al. Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results. Science of the Total Environment, 2015, 518: 626-635.
    [10]
    Lu X, Hu W Y, Huang B, et al. Analysis of heavy metal sources in farmland soils around mining area based on Unmix method. Environmental Science, 2018, 39(3): 1421-1429.
    [11]
    Tian F L, Chen J W, Liu C Y, et al. Application of Monte Carlo Uncertainty Analysis in the Source Analysis of Receptor models. Chinese Science Bulletin, 2011, 56(32): 2675-2680.
    [12]
    Khairy M A, Lohmann R. Source apportionment and risk assessment of polycyclic aromatic hydrocarbons in the atmospheric environment of Alexandria, Egypt. Chemosphere, 2013, 91(7): 895-903.
    [13]
    Yang B, Zhou L,Xue N, et al. Source apportionment of polycyclic aromatic hydrocarbons in soils of Huanghuai Plain, China: Comparison of three receptor models. Science of the Total Environment, 2013, 443: 31-39.
    [14]
    Chen W, Wu X, Zhang H, et al. Contamination characteristics and source apportionment of methylated PAHs in agricultural soils from Yangtze River Delta, China. Environmental Pollution, 2017, 230: 927-935.
    [15]
    Dong B, Zhang R Z, Gan Y D, et al. Multiple methods for the identification of heavy metal sources in cropland soils from a resource-based region. Science of the Total Environment, 2019, 651(2): 3127–3138.
    [16]
    Chen H Y, Teng Y G, Li J, et al. Source apportionment of trace metals in river sediments: A comparison of three methods. Environmental Pollution, 2016, 211:28-37.
    [17]
    Thurston G D, Spengler J D. A qualitative assessment of source contribution to inhalable particulate matter pollution in metropolitan Boston. Atmospheric Environment, 1985, 18: 1347-1355.
    [18]
    Paatero P, Tapper U. Positive matrix factorization: A non-negative factor method with optimal utilization of error estimates of data values. Environmetrics, 1994, 5(2): 111-126.
    [19]
    Tan J, Duan J, Ma Y, et al. Long-term trends of chemical characteristics and sources of fine particle in Foshan city, Pearl River Delta: 2008-2014. Science of the Total Environment, 2016, 565:519-528.
    [20]
    Vaccaro S, Sobiecka E, Contini S, et al. The application of positive matrix factorization in the analysis, characterization and detection of contaminated soils. Chemosphere, 2007, 69: 1055-1063.
    [21]
    Henry R C. Multivariate receptor modeling by N-dimensional edge detection. Chemometrics and Intelligent Laboratory Systems, 2003, 65: 179-189.
    [22]
    Lang Y H, Yang X, Wang H, et al. Diagnostic ratios and positive matrix factorization to identify potential sources of PAHs in sediments of the Rizhao offshore, China. Polycyclic Aromatic Compounds, 2013, 33: 161-172.
    [23]
    China National Environmental Monitoring Center (CNEMC). The Background Centrations of Soil Elements of China. Beijing: China Environmental Science Press, 1990.
    [24]
    Chen J J, Zhang H H, Liu J M, et al. Spatial distribution characteristics and influencing factors of heavy metal elements in soil surface under regional geological background in Guangdong Province. Journal of Ecology and Environment, 2011, 20(4): 646-651.
    [25]
    Tong Z Q, Gu L, Duan H J, et al. Spatial distribution of heavy metal concentrations in roadside soil based on Kriging interpolation: A case study of Zhengzhou-Kaifeng section of National Highway 310. Journal of Environmental Sciences, 2012, 32(12): 3030-3038.
    [26]
    Lin C J, Pehkonen S O. The chemistry of atmospheric mercury: A review. Atmospheric Environmental, 1999, 33: 2067-2079.
    [27]
    Kemp K. Trends and sources for heavy metals in urban atmosphere. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 2002, 189: 227-232.
    [28]
    Faiz Y, Tufail M, Javed M T, et al. Road dust pollution of Cd, Cu, Ni, Pb and Zn along Islamabad expressway, Pakistan. Microchemical Journal, 2009, 92(2): 186-192.
    [29]
    Cao Y Z, Liu X J, Xie Y F, et al. Analysis of composition and concentrations characteristics of polycyclic aromatic hydrocarbons in topsoil of main areas in China. Journal of Environmental Sciences, 2012, 32(1): 197-203.
    [30]
    Tian H Z, Wang Y, Xue Z G, et al. Atmospheric emissions estimation of Hg, As, and Se from coal-fired power plants in China,2007. Science of the Total Environment, 2011, 409:3078-3081.
    [31]
    Chen D Q, Xie Z Y, Zhang Y J, et al. Source apportionment of soil heavy metals in Guangzhou based on the PCA/APCS method and geostatistics. Ecology and Environmental Sciences, 2016, 25(6): 1014-1022.
    [32]
    Yuan Z W, Luo T, Liu X W, et al. Tracing anthropogenic cadmium emissions: From source to pollution. Science of the Total Environment, 2019, 676: 87-96.
    [33]
    Wei B, Yang L. A review of heavy metal contaminations in urban soils, urban road dusts and agricultural soils from China. Microchemical Journal, 2010, 94(2): 99-107.
    [34]
    Hu W Y, Wang H F, Dong L R, et al. Source identification of heavy metals in peri-urban agricultural soils of southeast China: An integrated approach. Environmental Pollution, 2018, 237: 650 - 661.
    [35]
    Bhattacharya P, Chesselet R, Stollenwerk K G, et al. Arsenic in the environment: Biology and chemistry. Science of the Total Environment, 2007, 379: 109-120.
    [36]
    Zheng Y M, Luo J F, Chen T B, et al. Characteristics of cadmium concentrations in soils of different land use types in Beijing. Geographical Research, 2005, 24(4): 542-548.
    [37]
    Markus J, Mcbratney A. A review of the contamination of soil with lead II. Spatial distribution and risk assessment of soil lead. Environment International, 2001, 27(5): 399-411.
    [38]
    Wang P L. Environmental geochemical characteristics of near-surface atmospheric dust in Chengdu. Chengdu: Chengdu University of Technology, 2004.
    [39]
    Gholizadeh M H, Melesse A M, Reddi L. Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida. Science of The Total Environment, 2016, 566–567: 1552-1567.
    Recommended articles
    TrendMD
    Volume 51 Issue 11 page: 813-821
    Cover

    Article Metrics

    Article views (220) PDF downloads(395)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return