IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i17p10502-d895584.html
   My bibliography  Save this article

Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification—A Case Study from Poland

Author

Listed:
  • Mariusz Sojka

    (Department of Land Improvement, Environmental Development and Spatial Management, Faculty of Environmental Engineering and Mechanical Engineering, Poznań University of Life Sciences, Piątkowska St. 94, 60-649 Poznań, Poland)

  • Joanna Jaskuła

    (Department of Land Improvement, Environmental Development and Spatial Management, Faculty of Environmental Engineering and Mechanical Engineering, Poznań University of Life Sciences, Piątkowska St. 94, 60-649 Poznań, Poland)

Abstract

This study investigated the spatial distribution, contamination, potential ecological risks and quantities of pollutant sources of six heavy metals (HMs) in sediments of 47 rivers. The catchments of the investigated rivers are situated in Poland, but some of them are located in Slovakia, the Czech Republic, and Germany. Cluster analysis was applied to analyze the spatial distribution of Cd, Cr, Cu, Ni, Pb, and Zn in river sediments. Moran I and Getis-Ord G i * statistics were calculated to reveal the distribution pattern and hotspot values. Principal component analysis (PCA) and positive matrix factorization (PMF) were used to identify pollution sources. Furthermore, geochemical indices and sediment quality guidelines allowed us to assess sediment contamination and potential toxic effects on aquatic biota. The results showed that in 1/3rd of the rivers, the HM pattern and concentrations indicate sediment contamination. The EF, PLI, and MPI indices indicate that concentrations were at a rather low level in 2/3rd of the analyzed rivers. Only in individual rivers may the HMs have toxic effects on aquatic biota. Spatial autocorrelation analysis using the Moran I statistic revealed a random and dispersed pattern of HMs in river sediments. PCA analysis identified two sources of HMs’ delivery to the aquatic environment. Cr, Cu, Ni, Pb, and Zn originate from point and non-point sources, while Cd concentrations have a dominant natural origin. The PMF identified three sources of pollution. Among them, urban pollution sources are responsible for Cu delivery, agricultural pollution for Zn, and industrial pollution for Ni and Cr. Moreover, the analysis showed no relationship between catchment land-use patterns and HM content in river sediments.

Suggested Citation

  • Mariusz Sojka & Joanna Jaskuła, 2022. "Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification—A Case Study from Poland," IJERPH, MDPI, vol. 19(17), pages 1-25, August.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10502-:d:895584
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/17/10502/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/17/10502/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yan Li & Liping Mei & Shenglu Zhou & Zhenyi Jia & Junxiao Wang & Baojie Li & Chunhui Wang & Shaohua Wu, 2018. "Analysis of Historical Sources of Heavy Metals in Lake Taihu Based on the Positive Matrix Factorization Model," IJERPH, MDPI, vol. 15(7), pages 1-12, July.
    2. Paweł Tomczyk & Bernard Gałka & Mirosław Wiatkowski & Bogna Buta & Łukasz Gruss, 2021. "Analysis of Spatial Distribution of Sediment Pollutants Accumulated in the Vicinity of a Small Hydropower Plant," Energies, MDPI, vol. 14(18), pages 1-20, September.
    3. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    4. Łukasz Borek & Tomasz Kowalik, 2022. "Hydromorphological Inventory and Evaluation of the Upland Stream: Case Study of a Small Ungauged Catchment in Western Carpathians, Poland," Land, MDPI, vol. 11(1), pages 1-21, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ivana Bolanča Mirković & Zdenka Bolanča, 2023. "Storage of Documents as a Function of Sustainability," Sustainability, MDPI, vol. 15(5), pages 1-16, February.
    2. Iolanda-Veronica Ganea & Ramona Bălc & Robert-Csaba Begy & Ioan Tanțău & Delia Maria Gligor, 2023. "Combining Contamination Indices and Multivariate Statistical Analysis for Metal Pollution Evaluation during the Last Century in Lacustrine Sediments of Lacu Sărat Lake, Romania," IJERPH, MDPI, vol. 20(2), pages 1-17, January.
    3. Cris Edward F. Monjardin & Christopher Power & Delia B. Senoro, 2023. "Spatio-Temporal Assessment of Manganese Contamination in Relation to River Morphology: A Study of the Boac and Mogpog Rivers in Marinduque, Philippines," Sustainability, MDPI, vol. 15(10), pages 1-26, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yong Bao & Xiaotian Liu & Lihong Yang, 2020. "Indirect Inference Estimation of Spatial Autoregressions," Econometrics, MDPI, vol. 8(3), pages 1-26, September.
    2. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2014. "Identification and Estimation of Outcome Response with Heterogeneous Treatment Externalities," EIEF Working Papers Series 1407, Einaudi Institute for Economics and Finance (EIEF), revised Sep 2014.
    3. Baltagi, Badi H. & Liu, Long, 2008. "Testing for random effects and spatial lag dependence in panel data models," Statistics & Probability Letters, Elsevier, vol. 78(18), pages 3304-3306, December.
    4. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    5. Badi H. Baltagi & Zhenlin Yang, 2013. "Standardized LM tests for spatial error dependence in linear or panel regressions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 103-134, February.
    6. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2020. "IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude," Monash Econometrics and Business Statistics Working Papers 11/20, Monash University, Department of Econometrics and Business Statistics.
    7. repec:asg:wpaper:1048 is not listed on IDEAS
    8. Shi, Wei & Lee, Lung-fei, 2018. "A spatial panel data model with time varying endogenous weights matrices and common factors," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 6-34.
    9. Allen Blackman & Beatriz Ávalos-Sartorio & Jeffrey Chow, 2012. "Land Cover Change in Agroforestry: Shade Coffee in El Salvador," Land Economics, University of Wisconsin Press, vol. 88(1), pages 75-101.
    10. Glass, Anthony J. & Kenjegalieva, Karligash & Ajayi, Victor & Adetutu, Morakinyo & Sickles, Robin C., 2016. "Relative Winners and Losers from Efficiency Spillovers in Africa with Policy Implications for Regional Integration," Working Papers 16-003, Rice University, Department of Economics.
    11. Debarsy, Nicolas & Jin, Fei & Lee, Lung-fei, 2015. "Large sample properties of the matrix exponential spatial specification with an application to FDI," Journal of Econometrics, Elsevier, vol. 188(1), pages 1-21.
    12. Baltagi, Badi H. & Yang, Zhenlin, 2013. "Heteroskedasticity and non-normality robust LM tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 725-739.
    13. Calkins, Lindsay N. & Ryan, Alexander J. & Zlatoper, Thomas J., 2023. "The Political Economy of Recreational Marijuana Laws in the U.S.: A Spatial Approach," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 53(01), April.
    14. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C. & Weyman-Jones, Thomas, 2018. "The Spatial Efficiency Multiplier and Common Correlated Effects in a Spatial Autoregressive Stochastic Frontier Model," Working Papers 18-003, Rice University, Department of Economics.
    15. Keller, Wolfgang & Shiue, Carol H., 2007. "The origin of spatial interaction," Journal of Econometrics, Elsevier, vol. 140(1), pages 304-332, September.
    16. Premand, Patrick & Stoeffler, Quentin, 2022. "Cash transfers, climatic shocks and resilience in the Sahel," Journal of Environmental Economics and Management, Elsevier, vol. 116(C).
    17. Guo, Penghui & Liu, Lihu, 2011. "Robust Test for Spatial Error Model:Considering Changes of Spatial Layouts and Distribution Misspecification," MPRA Paper 38050, University Library of Munich, Germany, revised Apr 2012.
    18. Crudu, Federico & Mellace, Giovanni & Sándor, Zsolt, 2021. "Inference In Instrumental Variable Models With Heteroskedasticity And Many Instruments," Econometric Theory, Cambridge University Press, vol. 37(2), pages 281-310, April.
    19. Nelson, Gerald C. & Geoghegan, Jacqueline, 2002. "Deforestation and land use change: sparse data environments," Agricultural Economics, Blackwell, vol. 27(3), pages 201-216, November.
    20. Wang, Honglin & Iglesias, Emma M. & Wooldridge, Jeffrey M., 2013. "Partial maximum likelihood estimation of spatial probit models," Journal of Econometrics, Elsevier, vol. 172(1), pages 77-89.
    21. Norman R. Swanson & John C. Chao, 2004. "Estimation and Testing Using Jackknife IV in Heteroskedastic Regressions with Many Weak Instruments," Econometric Society 2004 Far Eastern Meetings 668, Econometric Society.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10502-:d:895584. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.