The study of the different pollutants present in atmospheric aerosols such as trace elements and radionuclides is essential to assess the air quality. To analyze the particulate matter (PM), atmospheric filters with different dimensions and geometries (rectangular, circular, slotted, and square filters) are usually employed. Regarding the pollutants existing in atmospheric aerosols, radionuclides are usually analyzed due to their multiple applications such as either in the environmental radiological control or as tracers of atmospheric processes. Therefore, this study aims to develop a new and general methodology to calibrate in efficiency coaxial Ge detectors to properly determine radionuclides present in the PM by gamma-ray spectrometry for several filter types. For this, granular certified reference materials (CRM) containing only natural radionuclides ((238)U-series, (232)Th-series, and (40) K) were selected. Several granular solid CRMs were chosen allowing us to reproduce the same PM deposition geometry and to assure the homogeneity of the added CRMs. These are the main advantages in relation to the typical methods that use liquid CRMs. Furthermore, for filters whose surfaces are relatively large, they were cut in several pieces and placed one on top of the other, achieving the same geometry than the PM deposited onto the filter. Then, the experimental full-energy peak efficiencies (FEPEs) were obtained for each energy of interest (E(γ)) and they were fitted versus E(γ), finding a general FEPE function for each filter type. Finally, this methodology was validated for both natural and artificial radionuclides (from 46 to 1332 keV) by using different filter types employed in proficiency test exercises, obtaining |z(score)|<â2 for all cases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11869-023-01336-x.
A new efficiency calibration methodology for different atmospheric filter geometries by using coaxial Ge detectors.
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作者:Barba-Lobo A, BolÃvar J P
| 期刊: | Air Quality Atmosphere and Health | 影响因子: | 2.900 |
| 时间: | 2023 | 起止号: | 2023;16(6):1207-1214 |
| doi: | 10.1007/s11869-023-01336-x | ||
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