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1.
Wang, G.* et al.: Machine learning approaches for automatic classification of single-particle mass spectrometry data. Atmos. Meas. Tech. 17, 299-313 (2024)
2.
Liu, X. et al.: Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg. Atmos. Meas. Tech. 14, 5139-5151 (2021)
3.
Passig, J. et al.: Detection of ship plumes from residual fuel operation in emission control areas using single-particle mass spectrometry. Atmos. Meas. Tech. 14, 4171-4185 (2021)
4.
Bernauer, F. ; Hürkamp, K. ; Rühm, W. & Tschiersch, J.: On the consistency of 2-D video disdrometers in measuring microphysical parameters of solid precipitation. Atmos. Meas. Tech. 8, 3251-3261 (2015)
5.
Diab, J.* et al.: Hyphenation of a EC / OC thermal-optical carbon analyzer to photo-ionization time-of-flight mass spectrometry: An off-line aerosol mass spectrometric approach for characterization of primary and secondary particulate matter. Atmos. Meas. Tech. 8, 3337-3353 (2015)
6.
Yttri, K.E.* et al.: An intercomparison study of analytical methods used for quantification of levoglucosan in ambient aerosol filter samples. Atmos. Meas. Tech. 8, 125-147 (2015)
7.
Coen, M.C.* et al.: Minimizing light absorption measurement artifacts of the Aethalometer: Evaluation of five correction algorithms. Atmos. Meas. Tech. 3, 457-474 (2010)
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