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Strategic Planning of Methods for Monitoring and Assessing the Ecological State of Water Bodies

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Environmental safety related to ensuring the sustainable functioning of Water Management covers the areas of water protection and reproduction, rational use of Water Resources and development of water management and land reclamation, and contributes to the coordinated development and management of water, land and other related resources within river basins, as well as achieving maximum socio-ecological well-being, taking into account the interests of all water users. Water management is a special, specific branch of the Russian economy, because its development is directly related to the process of providing water to the population and all sectors of the economy. A special feature of the water management industry is its scale and connection with almost all sectors of the economy. Water management is particularly important in creating the necessary social and living conditions for the population. Water management is closely linked to the most important sectors of the economy, primarily the production form: industry and agriculture. Today, it has significant organizational shortcomings. It is managed, financed and technically directed by various ministries, agencies and services, as well as municipal enterprises of local authorities, which introduces a certain inconsistency in the rational use and protection of Water Resources and does not ensure proper order in ensuring environmental safety. Thus, the state of affairs in the field of water management requires drastic changes in the attitude of the state to the development of the water management complex and the reform of the water resources management system, since the development of this sector of the economy and, in general, the socio-economic development of the country depends on state regulation.
Słowa kluczowe
Twórcy
  • Center for Public Policy and Public Administration of the Institute of Social Sciences of the Russian Presidential Academy of National Economy and Public Administration (RANEPA), 84 Prospekt Vernadskogo, Moscow, 119571, Russia
  • Center for Public Policy and Public Administration of the Institute of Social Sciences of the Russian Presidential Academy of National Economy and Public Administration (RANEPA), 84 Prospekt Vernadskogo, Moscow, 119571, Russia
Bibliografia
  • 1. Abramov R.A. 2016. Regional economic policy based on industrial sector clustering in the context of sustainable development. Research Journal of Pharmaceutical, Biological and Chemical Sciences, 7(2), 2100–2106.
  • 2. Aharonovich, A.R. 2019. Socio-economic importance of state support for youth innovative entrepreneurship in the economic development of the state. Academy of Entrepreneurship Journal, 25 (Special Issue 1).
  • 3. Brack W. et al. 2016. Effect-directed analysis supporting monitoring of aquatic environments—an indepth overview. Sci Total Environ 544:1073–1118
  • 4. Bryman A. 2012. Social research methods. 4th ed. New York (NY): Oxford University Press.
  • 5. Busch W. et al. 2016. Micropollutants in European rivers: a mode of action survey to support the development of effect-based tools for water monitoring. Environ Toxicol Chem 35(8):1887–1899
  • 6. Escher B.I. et al. 2018. Effect-based trigger values for in vitro and in vivo bioassays performer on surface water extracts supporting the environmental quality standards (EQS) of the European Water Framework Directive. Sci Total Environ 628–629:748–765
  • 7. European Commission 2020. River basin network on water framework directive and agriculture. Joint Research Centre of the European Commission Report; Luxembourg; p. 266.
  • 8. Falck W.E., Spangenberg J.H. 2014. Selection of social demand-based indicators: EO-based indicators for mining. J Clean Prod. 84:193–203. https://doi.org/10.1016/j.jclepro.2014.02.021
  • 9. Gao J., Christensen P., Kørnøv L. 2014. The changing Chinese SEA indicator guidelines: top-down or bottom-up? Environ Impact Assess Rev. 44:22–30. https://doi.org/10.1016/j.eiar.2013.08.003
  • 10. Gleick P.H. 2014. The world’s water. Volume 8: the biennial report on freshwater resources. In: Gleick PH, editor. Washington (DC): Island Press; p. 496
  • 11. Juwana I., Muttil N., Perera B.J.C. 2012. Indicator-based water sustainability assessment – A review. Sci Total Environ. 438:357–371. https://doi.org/10.1016/j.scitotenv.2012.08.093
  • 12. Kase R. et al. 2018. Screening and risk management solutions for steroidal estrogens in surface and wastewater. Trends Anal Chem 102:343–358
  • 13. Könemann S. et al. 2018. Effect-based and chemical analytical methods to monitor estrogens under the European Water Framework Directive. Trends Anal Chem 102:225–235
  • 14. Mascarenhas A., Nunes L.M., Ramos T.B. 2015. Selection of sustainability indicators for planning: combining stakeholders’ participation and data reduction techniques. J Clean Prod. 92:295–307. https://doi.org/10.1016/j.jclepro.2015.01.005
  • 15. Morozov, I.V., Potanina, Y.M., Voronin, S.A., Kuchkovskaya, N.V., Siliush, M.D. 2018. Prospects for the development of the oil and gas industry in the regional and global economy. International Journal of Energy Economics and Policy, 8(4), 55-62.
  • 16. Muschket M. et al. 2018. Identification of unknown antiandrogenic compounds in surface waters by effect-directed analysis (EDA) using a parallel fractionation approach. Environ Sci Technol 52(1):288–297
  • 17. Muz M. et al. 2017. Identification of mutagenic aromatic amines in river samples with industrial wastewater impact. Environ Sci Technol 51(8):4681–4688
  • 18. Neale P.A. et al. 2017. Development of a bioanalytical test battery for water quality monitoring: fingerprinting identified micropollutants and their contribution to effects in surface water. Water Res 123:734–750
  • 19. Novák J. et al. 2018. Effect-based monitoring of the Danube River using mobile passive sampling. Sci Total Environ 636:1608–1619
  • 20. Ripetskii A.V. 2019. Preliminary geometric verification of the electronic model in additive manufacturing. Russian Engineering Research, 39(9), 789-792. https://doi.org/10.3103/S1068798X19090181
  • 21. Ripetskiy, A.V. 2018. Polygonal meshes data structure analysis used for computation of the parameters defining additive production process for different additive manufacturing technologies. Periodico Tche Quimica, 15 (Special Issue 1), 291-303.
  • 22. Rodnyansky, D., Abramov, R., Valeeva, G., Makarov, I., Levchegov, O. 2019. Methods to evaluate public administration efficiency: The case of the Volga region. International Journal of Engineering and Advanced Technology, 8(5), 2261-2271.
  • 23. Rodnyansky, D.V., Abramov, R.A., Repin, M.L., Nekrasova, E.A. 2019. Estimation of innovative clusters efficiency based on information management and basic models of data envelopment analysis. International Journal of Supply Chain Management, 8(5), 929-936
  • 24. Schulze T. et al. 2017. Assessment of a novel device for onsite integrative large-volume solid phase extraction of water samples to enable a comprehensive chemical and effect-based analysis. Sci Total Environ 581–582:350–358
  • 25. Shmygaleva, T.A., Kupchishin, A.I., Kupchishin, A. A., Shafii, C.A. 2019. Computer simulation of the energy spectra of (PKA) in materials irradiated by protons in the framework of the Cascade-robabilistic method. IOP Conference Series: Materials Science and Engineering, 510, 12024. https://doi.org/10.1088/1757-899x/510/1/012024
  • 26. Toušová Z. et al. 2019. Analytical and bioanalytical assessments of organic micropollutants in the Bosna River using a combination of passive sampling, bioassays and multi-residue analysis. Sci Total Environ 650:1599–1612
  • 27. van-Doren D., Driessen P.P.J., Schijf B., Runhaar H.C. 2013. Evaluating the substantive effectiveness of SEA: Towards a better understanding. Environ Impact Assess Rev. 38:120–130. https://doi.org/10.1016/j.eiar.2012.07.002
  • 28. Voronova, N.A., Kupchishin, A.I., Kupchishin, A. A., Kuatbayeva, A.A., Shmygaleva, T.A. 2018. Computer modeling of depth distribution of vacancy nanoclusters in ion-irradiated materials. Key Engineering Materials, 769, 358–363. https://doi.org/10.4028/www.scientific.net/kem.769.358
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-24191adf-b470-420b-a36e-7b2b87f9956f
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