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Panov A.V., Perevolotskaya T.V. Risk assessment methodology for agroecosystems in the conditions of technogenic pollution. Sel'skokhozyaistvennaya Biologiya [Agricultural Biology], 2020, Vol. 55, № 3, p. 468-480.
(how to cite this article)

doi: 10.15389/agrobiology.2020.3.468eng

UDC: 631.95:51-76

 

RISK ASSESSMENT METHODOLOGY FOR AGROECOSYSTEMS IN THE CONDITIONS OF TECHNOGENIC POLLUTION

A.V. Panov, T.V. Perevolotskaya

Russian Institute of Radiology and Agroecology, 109 km., Kievskoe shosse, Kaluga Province, Obninsk, 249032 Russia, e-mail: riar@mail.ru (✉ corresponding author), forest_rad@mail.ru

ORCID:
Panov A.V. orcid.org/0000-0002-9845-7572
Perevolotskaya T.V. orcid.org/0000-0001-8250-5536

Received July 27, 2019

 

Currently, the world scientific community is faced with the task of identifying and minimizing environmental risks of the impact of anthropogenic factors on ecosystems, especially agricultural ones (A.A. Muzalevsky et al., 2011). The keystone of environmental risk assessment is estimated probability of the adverse effects of various nature (radiation, chemical and biological agents) and prevention of their negative impact. The most common sources of pollution of agroecosystems are precipitation from industrial and transport emissions, industrial wastewater, sewage sludge, organic and mineral fertilizers and plant protection products, dumps of ash, slag, ores and slime (S.C. Barman et al., 2000; Yu.N. Vodyanitsky et al., 2011; E.C. Rowe et al., 2015). Such risks are usually assessed situationally, в The purpose of the presented theoretical research was to develop a unified methodology for assessing agroecological risks caused by anthropogenic pollutants. The developed methodology uses mathematical modeling methods and is based on the principles and criteria ensuring the safety of agricultural ecosystems in conditions of man-made pollution. Atmospheric pollutants are the main source of man-made impact. Temporal patterns of their impact vary from acute (e.g. upon accidents) to chronic that should be taken into account. Agroecosystem productivity, as an integral indicator, is a basic criterion for assessing agroecological risks. The methodology includes a four-step algorithm: i) hazard identification based on available agroecosystem data with identification of the sources and nature of the hazard and key affected components; ii) impact assessment by measuring or calculating its intensity, duration, and mode of exposure; iii) a dose-effect assessment by a relationship between the degree of the impact and the probability of its negative consequences; iv) risk characterization, including reliability analysis of the obtained data, estimates of risk from individual factors and their combinations, and calculation of a probability of adverse effect for each agroecosystem component. The choice of a method for assessing agroecological risks (deterministic, probabilistic of the 1st and 2nd type, and integral probabilistic) is substantiated based on the indicator pools, risk criteria and the degree of technogenic impact. Risk characterization includes its classification in terms of an environmentally acceptable level as per maximum allowed concentrations and semi-lethal doses (LD50). For each step, risk uncertainties are accounted. Agroecological risk assessment algorithm includes i) database analysis and selection of agent-specific and exposure-specific values of the effects; ii) estimates of meteorological parameters of pollutant diffusion under specific release conditions; iii) calculated or experimental estimates of pollutant deposition on the ground depending on peculiarities of the impact; iv) calculation or experimental assessment of radionuclide or chemical toxicant contaminations; v) calculation or measurement of the effects of radionuclides or chemical pollutants on the agroecosystem components. The proposed approaches to assessing agroecological risk are applicable to a wide class of environmental problems.

Keywords: agroecosystem, agricultural products, heavy metals, radionuclides, technogenic factor, agroecosystem components, impact level, dose-response relationships, mathematical models.

 

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