Predicting the toxicity of post-mining substrates, a case study based on laboratory tests, substrate chemistry, geographic information systems and remote sensing

0477397 - ÚVGZ 2018 RIV NL eng J - Článek v odborném periodiku
Tesnerová, C. - Zadinová, R. - Pikl, Miroslav - Zemek, František - Kadochová, Štěpánka - Matějíček, L. - Mihaljevič, M. - Frouz, Jan
Predicting the toxicity of post-mining substrates, a case study based on laboratory tests, substrate chemistry, geographic information systems and remote sensing.
Ecological Engineering. Roč. 100, Mar (2017), s. 56-62. ISSN 0925-8574. E-ISSN 1872-6992
Grant CEP: GA MŠMT(CZ) LO1415; GA MŠMT LM2015075
Výzkumná infrastruktura: CzeCOS II - 90061
Institucionální podpora: RVO:67179843 ; RVO:60077344
Klíčová slova: Mining * Toxicity * GIS * Hyperspectral data * Sinapis alba
Obor OECD: Environmental sciences (social aspects to be 5.7); Ecology (BC-A)
Impakt faktor: 3.023, rok: 2017

Approaches were evaluated for predicting the spatial distribution of phytotoxicity of post-mining substrates. Predictions were compared with empirical data measured in the field (a heap at a post-mining site) and laboratory. The study was performed in a highly variable 1-ha plot that was overlain with a regular grid of sampling points (with 5 m between adjacent grid points). At each of 21 points, soil pH, conductivity, and arsenic content were measured, and soil was sampled and used in a laboratory germination test with Sinapsis alba. At each grid point, a field germination test with S. alba was also conducted, and spontaneous vegetation was removed and weighed. At the same time, air-borne hyperspectral imagery data of the site were acquired, and field spectral characteristics of dominant substrates were measured. This enabled automatic substrate classification, which was used to map the spatial distribution of the substrates.S. alba germination in the laboratory was closely correlated with S. alba germination in the field (r = 0.918), and both were correlated with the biomass of spontaneously established vegetation in the field. Substrate pH and substrate type were the best predictors of S. alba germination at points between the grid points. S. alba germination was well predicted (P = 0.001) by (1) direct interpolation of toxicity between grid points (R-2 =0.51) and by (2) substrate classification based on hyperspectral images (R-2 = 0.56).
Trvalý link: http://hdl.handle.net/11104/0273750