Abstract
The aim of this work was to show how to construct maps of anthropogenic contamination of agricultural soils in Czech Republic by risk elements. The geochemical datasets for this work originated from state monitoring utilising acid extractions according to past and present national legislative requirements. The goal was to distinguish contamination from natural variability respecting knowledge in environmental geochemistry and available information sources. Conventional approaches to geochemical maps, such as plotting sampling points where element concentrations exceed Tukey or Carling upper fences (boxplot approach), can be used to visualise only extreme contamination, such as historical ore mining and processing. The challenge starts when weak and/or diffuse contamination is of interest and should be distinguished from natural variability. Geogenic anomalies in Czech Republic are represented by mafic volcanic rocks (Cd, Cu, Zn), metamorphic rocks (As, Zn), felsic intrusive volcanic rocks (Pb, Zn), and variegated rocks showing volcanic components (Cd, Pb, Zn). Lithological anomalies are typical for floodplain sediments of lowland rivers. Each cumulation of concentrations above the Tukey or Carling upper fences within the whole-Czech dataset, i.e. potential contamination hotspot, should be examined in detail to judge possible natural controls. Pleistocene and Holocene sediments, in particular aeolian and fluvial deposits with their specific grain size, represent an important controlling factor in such detailed maps. Element concentration ratios in rational subcompositions, e.g. including Co, Cu, Pb, and Zn, were found useful to separate geogenic and lithogenic anomalies, In this subcomposition, Co is promising reference element for datasets obtained by conventional acid extractions as a surrogate for missing analyses of lithogenic elements. There is no automated way of distinguishing anthropogenic contamination from natural variability for weak contamination, expert opinion is indispensable to distinguish natural and anthropogenic factors. The larger (more heterogeneous) the mapped areas, the more complicated interpretation of their geochemical maps and less reliable identification of anthropogenic contamination. Zooming in and examination of empirical cumulative distribution of element concentrations for the zoomed areas is the most powerful tool in converting geochemical to contamination maps, assuming the zoomed areas are covered by relatively homogeneous soils, with small number of soil-forming bedrock and not much geomorphic heterogeneity.
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Data availability
Dataset for this study (RKP) is available on request to Ministry of Agriculture of the Czech Republic.
References
Álvarez-Vázquez MÁ, Hošek M, Elznicová J et al (2020) Separation of geochemical signals in fluvial sediments: new approaches to grain-size control and anthropogenic contamination. Appl Geochem. https://doi.org/10.1016/j.apgeochem.2020.104791
Amorosi A, Guermandi M, Marchi N, Sammartino I (2014) Fingerprinting sedimentary and soil units by their natural metal contents: A new approach to assess metal contamination. Sci Total Environ 500–501:361–372. https://doi.org/10.1016/j.scitotenv.2014.08.078
Ander EL, Johnson CC, Cave MR et al (2013) Methodology for the determination of normal background concentrations of contaminants in English soil. Sci Tot Environ 454–455:604–618. https://doi.org/10.1016/j.scitotenv.2013.03.005
Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Aruta A, Albanese S, Daniele L et al (2022) A new approach to assess the degree of contamination and determine sources and risks related to PTEs in an urban environment: the case study of Santiago (Chile). Environ Geochem Health. https://doi.org/10.1007/s10653-021-01185-6
Bábek O, Grygar TM, Faměra M et al (2015) Geochemical background in polluted river sediments: How to separate the effects of sediment provenance and grain size with statistical rigour? Catena 135:240–253. https://doi.org/10.1016/j.catena.2015.07.003
Bednářová Z, Kalina J, Hájek O et al (2016) Spatial distribution and risk assessment of metals in agricultural soils. Geoderma 284:113–121. https://doi.org/10.1016/j.geoderma.2016.08.021
Borojerdnia A, Rozbahani MM, Nazarpour A, et al (2020) Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran. Sci Tot Environ. https://doi.org/10.1016/j.scitotenv.2020.140103
Bravo S, García-Ordiales E, García-Navarro FJ et al (2019) Geochemical distribution of major and trace elements in agricultural soils of Castilla-La Mancha (central Spain): finding criteria for baselines and delimiting regional anomalies. Environ Sci Pollut Res 26:3100–3114. https://doi.org/10.1007/s11356-017-0010-6
Brunsdon C, Fotheringham AS, Charlton M (2002) Geographically weighted summary statistics - a framework for localised exploratory data analysis. Comput Environ Urban Syst 26:501–524. https://doi.org/10.1016/S0198-9715(01)00009-6
Carling K (2000) Resistant outlier rules and the non-Gaussian case. Comput Stat Data Anal 33:249–258. https://doi.org/10.1016/S0167-9473(99)00057-2
Chen J, Gaillardet J, Bouchez J et al (2014) Anthropophile elements in river sediments: Overview from the Seine River, France. Geochem Geophys Geosys 15:4526–4546. https://doi.org/10.1002/2014GC005516
Cheng Q (2007) Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geol Rev 32:314–324. https://doi.org/10.1016/j.oregeorev.2006.10.002
Chlupáč I, Brzobohatý R, Kovanda J, Stráník Z (2011) Geologická minulost České republiky (Geological Past of the Czech Republic). Academia Praha. ISBN 978–80–200–1961–5
Faměra M, Matys Grygar T, Ciszewski D et al (2021) Anthropogenic records in a fluvial depositional system: The Odra River along The Czech-Polish border. Anthropocene. https://doi.org/10.1016/j.ancene.2021.100286
Filzmoser P, Garrett RG, Reimann C (2005) Multivariate outlier detection in exploration geochemistry. Comput Geosci 31:579–587. https://doi.org/10.1016/j.cageo.2004.11.013
Filzmoser P, Ruiz-Gazen A, Thomas-Agnan C (2014) Identification of local multivariate outliers. Stat Pap 55:29–47. https://doi.org/10.1007/s00362-013-0524-z
Filzmoser P, Hron K, Templ M (2018) Applied compositional data analysis. Springer, Cham. ISBN: 978-3-319-96422-5
Fügedi U, Kuti L, Vatai J et al (2012) No unique background in geochemistry. Carpathian J Earth Environ Sci 7:89–96
Gosar M, Šajn R, Bavec Š et al (2019) Geochemical background and threshold for 47 chemical elements in Slovenian topsoil. Geologija 62:5–57. https://doi.org/10.5474/geologija.2019.001
Greenacre M (2019) Variable selection in compositional data analysis using pairwise logratios. Math Geosci 51:649–682. https://doi.org/10.1007/s11004-018-9754-x
Grunsky EC, Mueller UA, Corrigan D (2014) A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: applications for predictive geological mapping. J Geochem Explor 141:15–41. https://doi.org/10.1016/j.gexplo.2013.07.013
Grygar T, Světlík I, Lisá L et al (2010) Geochemical tools for the stratigraphic correlation of floodplain deposits of the Morava River in Strážnické Pomoraví, Czech Republic from the last millennium. CATENA 80:106–121. https://doi.org/10.1016/j.catena.2009.09.005
Habibnia A, Rahimipour G, Ranjbar H (2020) Equivalence assessment and leveling of geochemical datasets to generate integrated geochemical maps: Application to mineral exploration. J Geochem Explor. https://doi.org/10.1016/j.gexplo.2020.106507
Haslett J, Bradley R, Craig P et al (1991) Dynamic graphics for exploring spatial data with application to locating global and local anomalies. Am Stat 45:234–242. https://doi.org/10.1080/00031305.1991.10475810
Hron K, Coenders G, Filzmoser P et al (2021) Analysing pairwise logratios revisited. Math Geosci. https://doi.org/10.1007/s11004-021-09938-w
Hron K, Machalová J, Menafoglio A (2022) Bivariate densities in Bayes spaces: orthogonal decomposition and spline representation. Stat Papers. https://doi.org/10.1007/s00362-022-01359-z
Johnson LE, Bishop TFA, Birch GF (2017) Modelling drivers and distribution of lead and zinc concentrations in soils of an urban catchment (Sydney estuary, Australia). Sci Tot Environ 598:168–178. https://doi.org/10.1016/j.scitotenv.2017.04.033
Kafka J (Editor) (2003) Rudné a uranové hornictví České republiky (Ore and Uranium Mining in the Czech Republic). DIAMO, Czech Republic, 647 pages. ISBN 80–86331–67–9
Kirkwood C, Cave M, Beamish D et al (2016) A machine learning approach to geochemical mapping. J Geochem Explor 167:49–61. https://doi.org/10.1016/j.gexplo.2016.05.003
Kylich T (2021) Vytvoření a vysvětlení mapy obsahů Cd a Cu v půdách ČR s důrazem na říční krajinu (Creation and explanation of soil maps of Cd and Cu content in Czech Republic with accent on fluvial landscape). MSc. Thesis. Faculty of Environment, J. E. Purkyně University in Ústí nad Labem
Kylich T (2022) Mapa znečištění půd ČR arsenem a zinkem z historických zdrojů: vytvoření a vysvětlení (Map of soil contamination by As ad Zn in Czech Republic: creation and explanation). MSc. Thesis. Faculty of Environment, J. E. Purkyně University in Ústí nad Labem
Laceby JP, McMahon J, Evrard O, Olley J (2015) A comparison of geological and statistical approaches to element selection for sediment fingerprinting. J Soils Sediments 15:2117–2131. https://doi.org/10.1007/s11368-015-1111-9
Lado LR, Hengl T, Reuter HI (2008) Heavy metals in European soils: a geostatistical analysis of the FOREGS Geochemical database. Geoderma 148:189–199. https://doi.org/10.1016/j.geoderma.2008.09.020
Lepeltier C (1969) A simplified statistical treatment of geochemical data by graphical representation. Economic Geol 64:538–550. https://doi.org/10.2113/gsecongeo.64.5.538
Matschullat J, Ottenstein R, Reimann C (2000) Geochemical background - Can we calculate it? Environ Geol 39:990–1000. https://doi.org/10.1007/s002549900084
Matthai C, Birch G (2001) Detection of anthropogenic Cu, Pb and Zn in continental shelf sediments off Sydney, Australia - A new approach using normalization with cobalt. Mar Pollut Bull 42:1055–1063. https://doi.org/10.1016/S0025-326X(01)00068-6
Matys Grygar T, Popelka J (2016) Revisiting geochemical methods of distinguishing natural concentrations and pollution by risk elements in fluvial sediments. J Geochem Explor 170:39–57. https://doi.org/10.1016/j.gexplo.2016.08.003
Matys Grygar T, Faměra M, Hošek M et al (2021) Uptake of Cd, Pb, U, and Zn by plants in floodplain pollution hotspots contributes to secondary contamination. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-14331-5
McKinley JM, Hron K, Grunsky EC et al (2016) The single component geochemical map: Fact or fiction? J Geochem Explor 162:16–28. https://doi.org/10.1016/j.gexplo.2015.12.005
Meyer H, Pebesma E (2021) Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods Ecol Evol 12:1620–1633. https://doi.org/10.1111/2041-210X.13650
Miesch AT (1981) Estimation of the geochemical threshold and its statistical significance. J Geochem Explor 16:49–76
Mikkonen HG, van de Graaff R, Clarke BO et al (2018) Geochemical indices and regression tree models for estimation of ambient background concentrations of copper, chromium, nickel and zinc in soil. Chemosphere 210:193–203. https://doi.org/10.1016/j.chemosphere.2018.06.138
Miloš B, Bensa A (2019) Background Variation and Threshold Values for Cadmium Concentration in Terra Rossa Soil from Dalmatia, Croatia. Eurasian Soil Sci 52:1622–1631. https://doi.org/10.1134/S1064229319120111
Minolfi G, Petrik A, Albanese S, et al (2018) The distribution of Pb, Cu and Zn in topsoil of the Campanian region, Italy. Geochemistry https://doi.org/10.1144/geochem2017-074
Négrel P, Sadeghi M, Ladenberger A et al (2015) Geochemical fingerprinting and source discrimination of agricultural soils at continental scale. Chem Geol 396:1–15. https://doi.org/10.1016/j.chemgeo.2014.12.004
Négrel P, Ladenberger A, Reimann C et al (2021) GEMAS: Geochemical distribution of Mg in agricultural soil of Europe. J Geochem Explor. https://doi.org/10.1016/j.gexplo.2020.106706
Němeček J, Vácha R, Podlešáková E (2010) Hodnocení kontaminace půd v ČR (Evaluation of Soil Contamination in CR). VÚMOP Praha. ISBN 978–80–86561–02–4
Panagos P, Ballabio C, Lugato E et al (2018) Potential sources of anthropogenic copper inputs to European agricultural soils. Sustainability. https://doi.org/10.3390/su10072380
Pereira B, Vandeuren A, Govaerts BB, Sonnet P (2016) Assessing dataset equivalence and leveling data in geochemical mapping. J Geochem Explor 168:36–48. https://doi.org/10.1016/j.gexplo.2016.05.012
Podlešáková E, Němeček J, Hálová G (1996) Návrh limitů kontaminace půd potenciálně rizikovými stopovými prvky pro ČR. (The proposal of soil contamination limits by potentially risky elements for CR) Rostlinna Vyroba 42:119–125
Rawlins BG, Webster R, Lister TR (2003) The influence of parent material on topsoil geochemistry in eastern England. Earth Surf Process Landf 28:1389–1409. https://doi.org/10.1002/esp.507
Reimann C, de Caritat P (2017) Establishing geochemical background variation and threshold values for 59 elements in Australian surface soil. Sci Tot Environ 578:633–648. https://doi.org/10.1016/j.scitotenv.2016.11.010
Reimann C, de Caritat P, Albanese S et al (2012) New soil composition data for Europe and Australia: Demonstrating comparability, identifying continental-scale processes and learning lessons for global geochemical mapping. Sci Tot Environ 416:239–252. https://doi.org/10.1016/j.scitotenv.2011.11.019
Reimann C, Fabian K, Birke M et al (2018) GEMAS: Establishing geochemical background and threshold for 53 chemical elements in European agricultural soil. Appl Geochem 88:730–740. https://doi.org/10.1016/j.apgeochem.2017.01.021
Reimann C, Fabian K, Flem B, Englmaier P (2019) The large-scale distribution of Cu and Zn in sub- and topsoil: Separating topsoil bioaccumulation and natural matrix effects from diffuse and regional contamination. Sci Tot Environ 655:730–740. https://doi.org/10.1016/j.scitotenv.2018.11.248
Reimann C, Filzmoser P, Garrett RG, Dutter R (2008) Statistical data analysis explained. Applied environmental statistics with R. John Wiley & Sons, Chichester. ISBN: 978-0-470-98581-6
Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1:206–215. https://doi.org/10.1038/s42256-019-0048-x
Rudnick RL, Gao S (2003) Treatise on Geochemistry: Composition of the continental crust. In: Rudnick RL, Holland HD, Turekian KK (eds) The Crust, Treatise on Geochemistry, 3. Elsevier-Pergamon, Oxford, pp 1–64
Saby NPA, Marchant BP, Lark RM et al (2011) Robust geostatistical prediction of trace elements across France. Geoderma 162:303–311. https://doi.org/10.1016/j.geoderma.2011.03.001
Sakan SM, Dević GJ, Relić DJ et al (2015) Environmental assessment of heavy metal pollution in freshwater sediment, Serbia. Clean-Soil Air Water 43:838–845. https://doi.org/10.1002/clen.201400275
Savignan L, Lee A, Coynel A et al (2021) Spatial distribution of trace elements in the soils of south-western France and identification of natural and anthropogenic sources. CATENA. https://doi.org/10.1016/j.catena.2021.105446
Sinclair AJ (1991) A fundamental approach to threshold estimation in exploration geochemistry: probability plots revisited. J Geochem Explor 41:1–22. https://doi.org/10.1016/0375-6742(91)90071-2
Sinclair AJ (1976) Applications of probability graphs in mineral exploration. Association of exploration geochemists, Issue 4. Richmond Printers Ltd., Richmond, B. C., Canada
Six L, Smolders E (2014) Future trends in soil cadmium concentration under current cadmium fluxes to European agricultural soils. Sci Tot Environ 485–486:319–328. https://doi.org/10.1016/j.scitotenv.2014.03.109
Skála J, Vácha R, Čechmánková J, Horváthová V (2020) Regional geochemical zonation of cultivated floodplains–Application of multi-element associations for soil quality evaluation along the Ohře (Eger) River, Czech Republic. J Geochem Explor https://doi.org/10.1016/j.gexplo.2020.106491
Skála J, Zádorová T, Žížala D (2021) On the interpretation of surprisingly high variation of soil map diversity in country-wide study of flood-affected agroecosystems using the legacy data in the Czech Republic. Geoderma. https://doi.org/10.1016/j.geoderma.2020.114732
Skála J, Vácha R, Hofman J, et al (2017) Spatial differentiation of ecosystem risks of soil pollution in floodplain areas of the Czech Republic. Soil Water Res 12: 1–9. https://doi.org/10.17221/53/2016-SWR
Spahić MP, Sakan S, Cvetković Ž et al (2018) Assessment of contamination, environmental risk, and origin of heavy metals in soils surrounding industrial facilities in Vojvodina. Serbia Environ Monit Assess. https://doi.org/10.1007/s10661-018-6583-9
Stanley CR, Sinclair AJ (1989) Comparison of probability plots and the gap statistic in the selection of thresholds for exploration geochemistry data. J Geochem Explor 32:355–357. https://doi.org/10.1016/0375-6742(89)90076-9
Sucharova J, Suchara I, Hola M et al (2011) Linking chemical elements in forest floor humus (Oh-horizon) in the Czech Republic to contamination sources. Env Pollution 159:1205–1214. https://doi.org/10.1016/j.envpol.2011.01.041
Száková J, Tlustoš P, Balík J et al (2000) Efficiency of extractants to release As, Cd and Zn from main soil compartments. Analusis 28:808–812. https://doi.org/10.1051/analusis:2000147
Thompson SK (2012) Sampling. 3rd Edition, John Wiley & Sons, Hoboken. ISBN:9780470402313. https://doi.org/10.1002/9781118162934
Tume P, Bech J, Reverter F et al (2011) Concentration and distribution of twelve metals in Central Catalonia surface soils. J Geochem Explor 109:92–103. https://doi.org/10.1016/j.gexplo.2010.10.013
Vácha R, Skála J, Čechmánková J et al (2015) Toxic elements and persistent organic pollutants derived from industrial emissions in agricultural soils of the Northern Czech Republic. J Soils Sediments 15:1813–1824. https://doi.org/10.1007/s11368-015-1120-8
Vdović N, Lučić M, Mikac N, Bačić N (2021) Partitioning of metal contaminants between bulk and fine-grained fraction in freshwater sediments: A critical appraisal. Minerals. https://doi.org/10.3390/min11060603
Vilà M, Martínez-Lladó X (2015) Approaching earth surface geochemical variability from representative samples of geological units: The Congost River basin case study. J Geochem Explor 148:79–95. https://doi.org/10.1016/j.gexplo.2014.08.013
Vöröš D, Geršlová E, Nývlt D et al (2019) Assessment of geogenic input into Bilina stream sediments (Czech Republic). Environ Monit Assess. https://doi.org/10.1007/s10661-019-7255-0
Wang H, Yilihamu Q, Yuan M, BaiH XuH, Wu J (2020) Prediction models of soil heavy metal(loid)s concentration for agricultural land in Dongli: A comparison of regression and random forest. Ecol Indicators. https://doi.org/10.1016/j.ecolind.2020.106801
Wilford J, de Caritat P, Bui E (2016) Predictive geochemical mapping using environmental correlation. Appl Geochem 66:275–288. https://doi.org/10.1016/j.apgeochem.2015.08.012
Zbíral J, Honsa I, Malý S, Čižmár D (2004) Soil Analysis III. Central Institute for Supervising and Testing in Agriculture, Brno (199 p, in Czech)
Zhang L, McKinley J, Cooper M et al (2020) A regional soil and river sediment geochemical study in Baoshan area, Yunnan province, southwest China. J Geochem Explor. https://doi.org/10.1016/j.gexplo.2020.106557
Zhang C, Luo L, Xu W, Ledwith V (2008) Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci Tot Environ 398:212-221. https://doi.org/10.1016/j.scitotenv.2008.03.011
Zinkutė R, Taraškevičius R, Jankauskaitė M et al (2020) Influence of site-classification approach on geochemical background values. Open Chem 18:1391–1411. https://doi.org/10.1515/chem-2020-0177
Zuo R, Xiong Y (2020) Geodata science and geochemical mapping. J Geochem Explor 209:12–24. https://doi.org/10.1016/j.gexplo.2019.106431
Zuo R, Wang J, Chen G, Yang M (2015) Identification of weak anomalies: A multifractal perspective. J Geochem Explor. https://doi.org/10.1016/j.gexplo.2014.05.005
Zuo R, Wang J, Xiong Y, Wang Z (2021) The processing methods of geochemical exploration data: past, present, and future. Appl Geochem. https://doi.org/10.1016/j.apgeochem.2021.105072
Acknowledgements
The authors are grateful to Ministry of Agriculture of the Czech Republic for providing the soil database for free for academic purposes. The authors from IIC Řež were supported by Czech Science Foundation within projects 19-01768S and 20-06728S; by the former one also K. Hron was supported. M. Á. Álvarez-Vázquez was supported by the Xunta de Galicia through the postdoctoral grant #ED481B-2019-066. J.Skála acknowledges financial support through the Technology Agency of the Czech Republic by grant No. SS03010364 under a funding programme “Prostředí pro život”(Environment for Life). TMG supervised the work and prepared the draft of the manuscript, JE prepared special maps and supervised RKP data processing in GIS, ŠT and TK prepared some maps, JS contributed by knowledge on RKP and data mining, KH provided statistical expertise, and MÁÁV contributed to manuscript preparation.
Funding
The work was performed under funding by Czech Science Foundation, projects 19-01768S and 20-06728S. M.Á. Álvarez-Vázquez was supported by the Xunta de Galicia through the postdoctoral grant #ED481B-2019–066. J.Skála acknowledges financial support through the Technology Agency of the Czech Republic by Grant No. SS03010364 under a funding programme “Prostředí pro život” (Environment for Life).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jitka Elznicová, Štěpánka Tůmová, Tomáš Kylich. Statistical concepts were refined by Jan Skála and Karel Hron. The first draft of the manuscript was written by Tomáš Matys Grygar and refined by Miguel Ángel Álvarez-Vázquez and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Matys Grygar, T., Elznicová, J., Tůmová, Š. et al. Moving from geochemical to contamination maps using incomplete chemical information from long-term high-density monitoring of Czech agricultural soils. Environ Earth Sci 82, 6 (2023). https://doi.org/10.1007/s12665-022-10692-3
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DOI: https://doi.org/10.1007/s12665-022-10692-3