Efficiency of health risk mitigation: complex assessment based on fuzzy sets theory and applied in planning activities aimed at ambient air protection

View or download the full article: 
PDF icon health-risk-analysis-2020-1-3.pdf
UDC: 
504.064: 614.7
DOI: 
10.21668/health.risk/2020.1.03.eng
Authors: 

N.V. Zaitseva1, M.A. Zemlyanova1,2, I.V. May1, V.B Alekseev1, P.V. Trusov2, E.V. Khrushcheva1, A.A. Savochkina2

Organization: 

1Federal Scientific Center for Medical and Preventive Health Risk Management Technologies, 82 Monastyrskaya Str., Perm, 614045, Russian Federation
2Perm National Research Polytechnic University, 29 Komsomolskiy Ave., Perm, 614990, Russian Federation

Abstract: 

When industrial objects emitting substantial masses of dust and gas mixtures are located within a settlement or close to its borders, it often results in poorer quality of the environment and damages to population health. Such a situation is typical for many cities in the country; primarily, for those that are included into “Pure air” Federal project, a part of the “Ecology” National project. Negative effects are produced by a set of various substances emitted from various industries. And it is quite often that great numbers of people are exposed to such emissions and as a result multiple and variable responses from their health are registered. Assessment of share contributions made by different emissions sources and each particular substance into aggregated negative responses from human health is a fundamental stage in assessing damages to health that occurred due to them; it is significant for working out an action plan aimed at hazardous impacts mitigation.
Given that, we proposed an approach based on fuzzy sets theory as a relevant methodological basis for assessing efficiency of risk mitigation and damage to health when planning and implementing activities aimed at ambient air protection. Application of this methodology allows assessing conditions of multi-component negative impacts producing multiple negative effects including direct damage done to human health. And here key parameters are assessed not as per point values but as per interval ones that are characterized with their belonging to a range of scaled parameters. Our research goal was to suggest methodical approaches to assessing efficiency of risk mitigation and damage to health when planning and implementing activities aimed at ambient air protection; the approaches were based on fuzzy sets theory. Results obtained via hygienic (field or calculated examinations of ambient air quality in settlements under exposure and beyond it) and epidemiologic (controlled medical and biological) research are taken as initial data for fuzzy modeling of multiple parameters ratios within “damage to health – mitigation efficiency” system. Principles applied for research design take into account key postulates of exposure assessment, “dose – effect” relationship for an influencing substance, a concept of exposure risk acceptability, peculiarities related to body reactions under combined aerogenic burdens, and plans for ambient air protection activities (including complex ones).
Comparing a list of substances that do actual damage to exposed population’s health with a list of substances included into plans on aggregated emissions reduction allows assessing adequacy; determining to what extent damage to health is mitigated allows assessing whether activities aimed at ambient air protection are sufficient and effective.

Keywords: 
damage to health, exposed population, ambient air contamination, mitigation, adverse effects, fuzzy sets theory, ambient air protection, adequacy, sufficiency, effectiveness
Zaitseva N.V., Zemlyanova M.A., May I.V., Alekseev V.B, Trusov P.V., Khrushcheva E.V., Savochkina A.A. Efficiency of health risk mitigation: complex assessment based on fuzzy sets theory and applied in planning activities aimed at ambient air protection. Health Risk Analysis, 2020, no. 1, pp. 25–37. DOI: 10.21668/health.risk/2020.1.03.eng
References: 
  1. Klyuev N.N., Yakovenko L.M. «Dirty» cities in Russia: factors determining air pollution. Vestnik Rossiiskogo universiteta druzhby narodov. Seriya: Ekologiya i bezopasnost' zhiznedeyatel'nosti, 2018, vol. 26, no. 2, pp. 237–250 (in Russian). DOI: 10.22363/2313-2310-2018-26-2-237-250
  2. Surzhikov V.D., Surzhikov D. V., Ibragimov S.S., Pananotti E.A. Air pollution as the factor of the influence on the life quality of the population. Byulleten' VSNTs SO RAMN, 2013, vol. 91, no. 3–2, pp. 135–139 (in Russian).
  3. Tsimmerman V.I., Badmaeva S.E. The impact of the industry branches on the city air environment. Vestnik KrasGAU, 2015, no. 4, pp. 3–6 (in Russian).
  4. Beelen R., Raaschou-Nielsen O., Stafoggia M., Andersen Z.J. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet, 2014, vol. 1, no. 383 (9919), pp. 785–795. DOI: 10.1016/S0140-6736(13)62158-3
  5. Air pollution and child health: prescribing clean air. World Health Organization, 2018. Available at: https://www.who.int/ceh/publications/air-pollution-child-health/en/ (10.03.2020).
  6. Götschi T., Heinrich J., Sunyer J., Künzli N. Long-term effects of ambient air pollution on lung function: a review. Epidemiology, 2008, vol. 19, no. 5, pp. 690–701. DOI: 10.1097/EDE.0b013e318181650f
  7. Avaliani S.L., Revich B.A., Mishina A.L. The role of assessment of pro rata contribution of emissions from businesses outside the study area of the city, into various kinds of risks to public health. Zdorov'e naseleniya i sreda obitaniya, 2010, no. 11 (212), pp. 41–43 (in Russian).
  8. Analiz riska zdorov'yu v strategii gosudarstvennogo sotsial'no-ekonomicheskogo razvitiya [Health risk analysis in the strategy for state social and economic development: a monograph]. In: G.G. Onishchenko, N.V. Zaitseva eds. Perm, PNIPU Publ., 2014, 738 p. (in Russian).
  9. Goryaev D.V., Tikhonova I.V., Kir'yanov D.A. Industrial enterprises and health risk categories. Gigiena i sanitariya, 2017, vol. 96, no. 12, pp. 1155–1158 (in Russian). DOI: 10.18821/0016-9900-2017-96-12-1155-1158
  10. Popova A.Yu., Zaitseva N.V., May I.V. experience of methodological support and practical implementation of the risk-oriented model of sanitary-epidemiological surveillance in 2014-2017. Gigiena i sanitariya, 2018, vol. 97, no. 1, pp. 5–9 (in Russian).
  11. Kamaltdinov M.R., Kir'yanov D.A. Health risk assessment in violation of the legislation in the sphere of ensuring sanitary-epidemiological well-being of the population carried out to classify objects surveillance. Zdorov'e naseleniya i sreda obitaniya, 2015, vol. 273, no. 12, pp. 8–11 (in Russian).
  12. Kol'dibekova Yu.V., Zemlyanova M.A., Ignatova A.M., Tikhonova I.V., Markovich N.I., Chetverkina K.V., Ukhabov V.M. Assessment of the risk for health disorders in children who live in a territory of the zone of exposure to production of metallurgical aluminum. Gigiena i sanitariya, 2019, vol. 98, no. 2, pp. 135–141 (in Russian). DOI: 10.18821/0016-9900-2019-98-2-135-141
  13. Onishchenko G.G., Novikov S.M., Rakhmanin Yu.A., Avaliani S.L., Bushtueva K.A. Osnovy otsenki riska dlya zdorov'ya naseleniya pri vozdeistvii khimicheskikh veshchestv, zagryaznyayushch ikh okruzhayushchuyu sredu [Assessing risks of health disorders in children living in a zone exposed to metallurgic alumina production]. In: Yu.A. Rakhmanina, G.G. Onishchenko eds. Moscow, NII ECh i GOS Publ., 2002, 408 p. (in Russian).
  14. Mamyrbaev A.A.., Sakebaeva L.D., Sabyrakhmetova V.M., Karashova G.I., Shayakhmetova K.N., Umarova G.A. As-sessment of risk of non-carcinogenic effects due to the pollution of atmospheric air in residential areas of Uralsk city. Meditsinskii zhurnal Zapadnogo Kazakhstana, 2016, vol. 49, no. 1, pp. 82–88 (in Russian).
  15. Kliucininkas L., Velykiene D. Environmental health damage factors assessment in brownfield redevelopment. Pro-ceedings WIT Transactions on Biomedicine and Health, 2009, vol. 14, pp. 179–186. DOI: 10.2495/EHR090181
  16. Fabisiak J.P., Jackson E.M., Brink L.L., Presto A.A. A risk-based model to assess environmental justice and coronary heart disease burden from traffic-related air pollutants. Environ Health, 2020, vol. 16, no. 19 (1), pp. 34. DOI: 10.1186/s12940-020-00584-z
  17. Lucas R.M, McMichael A.J. Association or Causation: evaluating links between «environment and disease». Bulletin of the World Health Organization: the International Journal of Public Health, 2005, vol. 83, no. 10, pp. 792–795.
  18. Hill A.B. The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 1965, vol. 58, pp. 295–300.
  19. Science for Environment Policy. The precautionary principle: decision-making under uncertainty. Available at: https://ec.europa.eu/environment/integration/research/newsalert/pdf/prec... (20.03.2020).
  20. Simankov V.S., Buchatskaya V.V., Teploukhov S.V. Approach to the accounting for initial information uncertainty in system researches. Vestnik Adygeiskogo gosudarstvennogo universiteta. Seriya 4: Estestvenno-matematicheskie i tekhnicheskie nauki, 2017, vol. 3, no. 206, pp. 100–107 (in Russian).
  21. Shoina I.I. Risk appraisal in conditions of uncertainty. Nauchnyi vestnik MGTU GA, 2006, no. 106, pp. 165–169 (in Russian).
  22. Kuz'min E.A. The problem of uncertainty as a scientific category. Strategicheskie resheniya i risk-menedzhment, 2014, no. 3, pp. 90–100 (in Russian).
  23. Kleyn S.V., Zaitseva N.V., May I.V. Questions form of evidence of harm to public health in terms of ecological trouble. Okhrana okruzhayushchei sredy i prirodopol'zovanie, 2013, no. 2, pp. 28–32 (in Russian).
  24. Zaitseva N.V., Zemlyanova M.A., Luzhetskii K.P., Kleyn S.V. Scientific justification of the exposure and effect bi-omarkers in terms of proving health impact when identifying environmentally-determined unacceptable risk. Vestnik Permskogo universiteta. Seriya Biologiya, 2016, no. 4, pp. 374–378 (in Russian).
  25. Bondarenko P.V., Fokina E.A., Trukhlyaeva A.A. Application of the theory of fuzzy sets for assessment of the quality of life population of the region. Fundamental'nye issledovaniya, 2015, no. 11–5, pp. 967–971 (in Russian).
  26. Zadeh L., Bellman R. Decision-making in a fuzzy environment. Management Science, 1970, vol. 17, no. 4, pp. 41–164. DOI:10.1287/mnsc.17.4.B141
  27. Zade L.A. Ponyatie lingvisticheskoi peremennoi i ego primenenie k prinyatiyu priblizhennykh reshenii [A concept of linguistic variable and its application in getting approximate solutions]. In: N.N. Moiseev, S.A. Orlovskii eds. Moscow, Mir Publ., 1976, 165 p. (in Russian).
  28. Diligenskii N.V., Dymova L.G., Sevast'yanov P.V. Nechetkoe modelirovanie i mnogokriterial'naya optimizatsiya proizvodstvennykh sistem v usloviyakh neopredelennosti: tekhnologiya, ekonomika, ekologiya [Fuzzy modeling and multi-criteria optimization of industrial systems under uncertainty: technology, economy, and ecology]. Available at: http://os.x-pdf.ru/20ekonomika/411551-1-diligenskiy-dimova-sevastyanov-n... (04.03.2020) (in Russian).
  29. Tah J.H.M., Carr V. A proposal for construction project risk assessment using fuzzy logic. Construction Management & Economics, 2000, vol. 18, no. 4, pp. 491–500. DOI: 10.1080/01446190050024905
  30. Kochubei N.A. Modeli prinyatiya reshenii na osnove nechetkikh mnozhestv [Models for decision-making based on fuzzy sets]. Ekonomicheskii analiz: teoriya i praktika, 2010, vol. 17, no. 182, pp. 63–67 (in Russian).
Received: 
18.02.2020
Accepted: 
24.03.2020
Published: 
30.03.2020

You are here

Home