The effect of technological oil spill in soil within electrical generation substations, analysed by ecological regime in the context of relief properties

Keywords: digital elevation model; environmental regimes; phytoindication; landforms; spatial models.


Technological oil spills within electrical substations are the source of considerable environmental contamination. The aim of this study is to evaluate the relation between phytoindication assessments of ecological factors and geomorphological covariates and investigate the effect of the technological oil spill on ecological regimes within electrical substations. During the fieldwork 175 geobotanical releves were analysed in the years 2016–2017 within Dnipropetrovsk region (Ukraine). Within each electrical substation the geobotanical prospecting was conducted both in plots with undisturbed vegetation cover (control, the plot size 3 × 6 m) and in plots with technological oil spill (pollution, plot size 3 × 3 m). Phytoindication assessment of the following ecological factors was made: soil water regime, soil aeration, soil acidity, total salt regime, carbonate content in the soil, nitrogen content in the soil, radiation balance, aridity or humidity, continental climate, cryo-climate, light regime. HydroSHEDS data were taken for the basis for creating a digital elevation model with resolution of the data layer 15 arcseconds. The phytoindication assessments of the ecological regimes are characterized by correlation of geomorphological properties. The soil humidity is characterized by statistically significant negative correlation with the topographic position index and positive correlation with the vector ruggedness measure. The variability of damping correlates with four geomorphological predictors. This environmental regime has positive correlation with digital elevation model and diffuse insolation and negative correlation with topographic wetness index and direct insolation. The soil acidity of the edaphotope within Dnipropetrovsk region correlates with statistical signiicance with the vector ruggedness measure. The soil humidity of the edaphotope is associated with variation of the topographic wetness index, direct insolation, diffuse insolation and entropy of terrain diversity. The highest carbonate content in the soil correlates with higher risks of erosion, which is characterized by loss of soil and vertical distance to channel network. The nitrogen content in the soil is very sensitive to geomorphological features of the area. This results in the correlation of this indicator with six geomorphological predictors. Obviously, the most favourable supply of the nitrogen content in the soil is formed on upland areas. This allows positive correlation of the phytoindication assessment of the nitrogen content in the soil and the height relief. The use of relief variable as the covariate revealed the nature of the impact of soil contamination on ecological factors. Technological oil pollution leads to deterioration of water regime, reducing the availability of plant available forms of nitrogen and deterioration of soil aeration. There are also changes in microclimatic properties. There are more extreme thermal regimes and greater level of illumination. A key task for further research is to study the influence of relief features on the degree of negative transformation of soil due to technological oil pollution.


Abbasi Maedeh, P., Nasrabadi, T., Wu, W., & Al Dianty, M. (2017). Evaluation of oil pollution dispersion in an unsaturated sandy soil environment. Pollution, 3(4), 701–711.
Abosede, E. E. (2013). Effect of crude oil pollution on some soil physical properties. Journal of Agriculture and Veterinary Science, 6(3), 14–17.
Andrushenko, A. Y., & Zhukov, A. V. (2016). Scale-dependent effects in structure of the wintering ecological niche of the mute swan during wintering in the gulf of Sivash. Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University, 6(3), 234–247.
Austin, M. P. (1976). Non-linear species response models in ordination. Vegetatio, 33, 33–41.
Baljuk, J. A., Kunah, O. N., Zhukov, A. V., Zadorozhnaja, G. A., & Ganzha, D. S. (2014). Sampling adaptive strategy and spatial organisation estimation of soil animal communities at various hierarchical levels of urbanised territories. Biological Bulletin, 4(3), 8–33.
Beven, K., & Kirkby, N. (1979). A physically based variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24, 43–69.
Bock, M., & Köthe, R. (2008). Predicting the depth of hydrologic soil characteristics. Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie, 19, 13–22.
Boehner, J., & Antonic, O. (2009). Land surface parameters specific to topo-climatology. In: Hengl, T., & Reuter, H. I. (Eds.). Geomorphometry – concepts. Software, Applications. Pp. 195–226.
Brown, P. J., Long, S. M., Spurgeon, D. J., Svendsen, C., & Hankard, P. K. (2004). Toxicological and biochemical responses of the earthworm Lumbricus rubellus to pyrene, a non-carcinogenic polycyclic aromatic hydrocarbon. Chemosphere, 57, 1675–1681.
Brygadyrenko, V. V. (2015). Community structure of litter invertebrates of forest belt ecosystems in the Ukrainian steppe zone. International Journal of Environmental Research, 9(4), 1183–1192.
Brygadyrenko, V. V. (2016). Evaluation of ecological niches of abundant species of Poecilus and Pterostichus (Coleoptera: Carabidae) in forests of the steppe zone of Ukraine. Entomologica Fennica, 27(2), 81–100.
Buzuk, G. N. (2017). Phytoindication with ecological scales and regression analysis: Environmental index. Bulletin of Pharmacy, 76, 31–37.
Chi, B. L., Bing, C. S., Walley, F., & Yates, T. (2009). Topographic indices and yield variability in a rolling landscape of western Canada. Pedosphere, 19(3), 362–370.
Ciha, A. J. (1984). Slope position and grain yield of soft white winter wheat. Agronomy Journal, 76, 193–196.
Contreras-Ramos, S. M., Alvarez-Bernal, D., & Dendooven, L. (2006). Eisenia fetida increased removal of polycyclic aromatic hydrocarbons from soil. Environmental Pollution, 141, 396–401.
Cox, M. S., Gerard, P. D., & Abshire, M. J. (2006). Selected soil properties’ variability and their relationships with yield in three Mississippi fields. Soil Science Society of America Journal, 171, 541–551.
Davis, L. C., Castro-Diaz, S., Zhang, Q., & Erickson, L. E. (2002) Benefits of vegetation for soils with organic contaminants. Critical Reviews in Plant Sciences, 21(5), 457–491.
Dehn, M., Gärtner, H., & Dikau, R. (2001). Principles of semantic modeling of landform structures. Computers and Geoscience, 27, 1005–1010.
Dendooven, L., Alvarez-Bernal, D., & Contreras-Ramos, S. M. (2011). Earthworms, a means to accelerate removal of hydrocarbons (PAHs) from soil? A mini-review. Pedobiologia, 54, 187–192.
Didukh, Y. P. (2011). The ecological scales for the species of Ukrainian flora and their use in synphytoindication. Phytosociocentre, Kyiv.
Fismes, J., Perrin-Ganier, C., Empereur-Bissonnet, P., & Morel, J. L. (2002). Soil-to-root transfer and translocation of polycyclic aromatic hydrocarbons by vegetables grown on industrial contaminated soils. Journal of Environmental Quality, 31, 1649–1656.
Green, T. R., & Erskine, R. H. (2004). Measurement, scaling, and topographic analyses of spatial crop yield and soil water content. Hydrological Processes, 18, 1447–1465.
Griffith, D. A., & Arbia, G. (2010). Detecting negative spatial autocorrelation in georeferenced random variables. International Journal of Geographical Information Science, 24(3), 417–437.
Guisan, A., Weiss, S. B., & Weiss, A. D. (1999). GLM versus CCA spatial modeling of plant species distribution. Plant Ecology, 143, 107–122.
Halvorson, G. A., & Doll, E. C. (1991). Topographic effects on spring wheat yield and water use. Soil Science Society of America Journal, 55, 1680–1685.
Han, G., Cui, B. X., Zhang, X. X., & Li, K. R. (2016). The effects of petroleum-contaminated soil on photosynthesis of Amorpha fruticosa seedlings. International Journal of Environmental Science and Technology, 13(10), 2383–2392.
Karatzoglou, A., Smola, A., Hornik, K., & Zeileis, A. (2004). Kernlab – An S4 package for Kernel methods in R. Journal of Statistical Software, 11(9), 1–20.
Kaspar, T. C., Pulido, D. J., Fenton, T. E., Colvin, T. S., Karlen, D. L., Jaynes, D. B., & Meek, D. W. (2004). Relationships of corn and soybean yield to soil and terrain properties. Agronomy Journal, 96, 700–709.
Kipopoulou, A. M., Manoli, E., & Samara, C. (1999). Bioconcentration of polycyclic aromatic hydrocarbons in vegetables grown in an industrial area. Environmental Pollution, 106(3), 369–380.
Klamerus-Iwan, A., Błońska, E., Lasota, J., Kalandyk, A., & Waligórski, P. (2015). Influence of oil contamination on physical and biological properties of forest soil after chainsaw use. Water, Air, and Soil Pollution, 226(11), 389.
Kravchenko, A. N., & Bullock, D. G. (2000). Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal, 92, 75–83.
Kukharchik, T. I., Kakareka, S. V., Khomich, V. S., Kurman, P. V., & Voropai, E. N. (2007). Polychlorinated biphenyls in soils of Belarus: Sources, contamination levels, and problems of study. Eurasian Soil Science, 40(5), 485–492.
Kunah, O. M., & Papka, O. S. (2016). Ecogeographical determinants of the ecological niche of the common milkweed (Asclepias syriaca) on the basis of indices of remote sensing of land images. Visnyk of Dnipropetrovsk University, Biology, Ecology, 24(1), 78–86.
Kunah, O. M., & Papka, O. S. (2016). Geomorphological ecogeographical variables definig features of ecological niche of common milkweed (Asclepias syriaca L.). Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University, 1, 243–275.
Legendre, P. (1993). Spatial autocorrelation: Trouble or new paradigm? Ecology, 74(6), 1659–1673.
Lehner, B., Verdin, K., & Jarvis, A. (2006). HydroSHEDS Technical Documentation. World Wildlife Fund US, Washington.
Lennon, J. J. (2000). Red-shifts and red herrings in geographical ecology. Ecography, 23, 101–113.
Ließ, M., Schmidt, J., & Glaser, B. (2016). Improving the spatial prediction of Soil organic carbon stocks in a complex tropical mountain landscape by methodological specifications in machine learning approaches. PLoS One, 11(4), e0153673.
Lipińska, A., Kucharski, J., & Wyszkowska, J. (2013). Urease activity in soil contaminated with polycyclic aromatic hydrocarbons. Polish Journal of Environmental Studies, 22(5), 1393–1400.
Loick, N., Hobbs, P. J., Hale, M. D. C., & Jones, D. L. (2009). Bioremediation of poly-aromatic hydrocarbon (PAH)-contaminated soil by composting. Critical Reviews in Environmental Science and Technology, 39(4), 271–332.
Magee, B. R., Lion, L. W., & Lemley, A. T. (1991). Transport of dissolved organic macromolecules and their effect on the transport of phenanthrene in porous media. Environmental Science and Technology, 25(2), 323–331.
Marques da Silva, J. R., & Silva, L. L. (2006). Evaluation of maize yield spatial variability based on field flow density. Biosystems Engineering, 95, 339–347.
McBratney, A. B., Mendonca-Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma,. 117, 3–52.
McCool, D. K., Renard, K. G., & Foster, G. R. (1994). The revised universal soil loss equation. In: Proceedings of an international workshop on soil erosion. The Center for Technology Transfer and Pollution Prevention, Purdue University, West Lafayette, USA. Pp. 45–59.
Miao, Y. X., Mulla, D. J., & Robert, P. C. (2006). Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precision Agriculture, 7, 117–135.
Moore, I. D., Nortin, T. W., & Williams, J. E. (1993). Modelling environmental heterogeneity in forested landscapes. Journal of Hydrology, 150, 717–747.
Moore, I., Gessler, P., Nielsen, G., & Peterson, G. (1993). Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57, 443–452.
Mueller, K. E., & Shann, J. R. (2006). Polycyclic aromatic hydrocarbons in spiked soil: Impacts of bioavailability, microbial activity and trees. Chemosphere, 64, 1006–1014.
Nam, J. J., Song, B. H., Eom, K. C., Lee, S. H., & Smith, A. (2003). Distribution of polycyclic aromatic hydrocarbons in agricultural soils in South Korea. Chemosphere, 50, 1281–1289.
Olaya, V., & Conrad, O. (2008). Geomorphometry in SAGA. In: Hengl, T., & Reuter, H. I. (Eds.). Geomorphometry: Concepts, software, applications. Elsevier.
Orlanski, J. (1975). A rational subdivision of scales for atmospheric processes. Bulletin of the American Meteorological Society, 56, 527–530.
Potapenko, O. V. (2016). Assessment of environmental conditions within the boundaries of electric substations methods phytoindication. Visnyk of Dnipropetrovsk State Agrаrian and Economic University, 42, 133–139.
Potapenko, O. V. (2018). Assessment of phytocoenonical diversity of electrical substations territories. Acta Biologica Sibirica, 4(3), 6–35.
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Sappington, J. M., Longshore, K. M., & Thompson, D. B. (2007). Quantifying landscape ruggedness for animal habitat analysis: A case study using desert bighorn sheep in the Mojave Desert. Journal of Wildlife Management, 71(5), 1419–1426.
Simmons, F. W., Cassel, D. K., & Daniels, R. B. (1989). Landscape and soil property effects on corn grain yield response to tillage. Soil Science Society of America Journal, 53, 534–539.
Srogi, K. (2007). Monitoring of environmental exposure to polycyclic aromatic hydrocarbons: A review. Environmental Chemistry Letters, 5(4), 169–195.
Ter Braak, C. J. F. (1986). Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167–1179.
Timlin, D., Pachepsky, Y., Snyder, V. A., & Bryant, R. B. (1998). Spatial and temporal variability of corn grain yield on a hillslope. Soil Science Society of America Journal, 62, 764–773.
Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(1), 234–240.
Wcisło, E. (1998). Soil contamination with polycyclic aromatic hydrocarbons (PAHs) in Poland. Polish Journal of Environmental Studies, 7(5), 267–272.
Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses. In: Agricultural handbook. Washington. Pp. 537–565.
Zamotaev, I. V., Ivanov, I. V., Mikheev, P. V., & Nikonova, A. N. (2015). Chemical contamination and transformation of soils in hydrocarbon production regions. Eurasian Soil Science, 48(12), 1370–1382.
Zeleke, T. B., & Si, B. C. (2004). Scaling properties of topographic indices and crop yield: Multifractal and joint multifractal approaches. Agronomy Journal, 96, 1082–1090.
Zhukov, A. V., & Zadorozhnaya, G. A. (2016). Spatio-temporal dynamics of the penetration resistance of recultivated soils formed after open cast mining. Visnyk of Dnipropetrovsk University, Biology, Ecology, 24(2), 324–331.
Zhukov, A. V., Kunah, O. N., Novikova, V. A., & Ganzha, D. S. (2016). Phytoindication estimation of soil mesopedobionts communities catena and their ecomorphic organization. Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University, 6(3), 91–117.
Zhukov, A. V., Sirovatko, V. O., & Ponomarenko, N. O. (2017). Spatial dynamic of the agriculture fields towards their shape and size. Ukrainian Journal of Ecology, 7(3), 14–31.
Zhukov, A. V., & Andryushchenko, A. Y. (2017). Relief and ecological niche of mute swan (Cygnus olor (Gmelin, 1803)) wintering in Sivash. Acta Biologica Sibirica, 3(2), 20–45.
Zhukov, O. V., & Potapenko, O. V. (2017). Environmental impact assessment of distribution substations: The case of phytoindication. Ukrainian Journal of Ecology, 7(1), 5–21.
Zhukov, O. V., Kunah, O. M., Taran, V. O., & Lebedinska, M. M. (2016). Spatial variability of soils electrical conductivity within arena of the river Dnepr valley (territory of the nature reserve “Dniprovsko-Orilsky”). Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University, 6(2), 129–157.

Most read articles by the same author(s)