환경배출범주/특수환경배출범주 기반 ECETOC TRA 및 주요/산업/용도 분류체계 이용의 K-CHESAR에 의한 환경예측농도 비교 연구

A Comparative Study of Predicted Environmental Concentrations from ECETOC TRA Based on Environmental Release Categories/Specific Environmental Release Categories and K-CHESAR Using Main/Industrial/Use Categories

Background: Environmental concentrations of substances can be estimated by K-CHESAR based on main, industrial, and use categories (MC/IC/UC) and ECETOC TRA based on environmental or specific environmental categories (ERC or spERC). Objectives: Three different systems for estimating environmental concentrations were compared to figure out their order with possible reasons along with relationship of regional predicted environmental concentrations (PECregional) and final PEClocal for various uses of a substance. Methods: Typical uses of the case substance and their corresponding ERCs were selected from the webpage of the European Chemical Agency. Proper MC/IC/UC and spERC were assigned to each ERC. Emission fractions were compared for each assessment code from the available database. PECs were calculated by three estimating systems: K-CHESAR using MC/IC/UC, ECETOC TRA using ERC, and ECETOC TRA using spERC with their default values for input parameters. Percentage of PECregional to PEClocal were manually calculated for each use. Results: Emission factors decreased in the order of ERC > MC/IC/UC > spERC. Values of the final PEClocal derived as sum of PECregional and Clocal decreased in the order of calculations using ECETOC TRA-ERC>KCHESAR with MC/IC/UC>ECETOC TRA-spERC for all environmental media. Percentages of PECregional,water to PEClocal,water ranged from 0 to 10.3% in industrial uses calculated with MC/IC/UC and ERC but 96.3 to 100% in wide dispersive uses of ERC and spERC where values of Clocal,water are estimated to be very low. Conclusions: ECETOC TRA generated the most refined PNEC values with spERC and the least with ERC, while K-CHESAR with MC/IC/UC generated values between the two results. The ratio of PECregional to PEClocal can be a good measure for performing suitable estimation of PNECs according to use.