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Peer-reviewedCC BY 4.0Aerosol Research

Look-up tables for complex refractive index correction of particle sizes measured by common research-grade optical particle counters

Abstract

Optical particle counters (OPCs) are widely used to measure the aerosol particle number size distribution over a large size range encompassing sub- and super-micron diameters. The measurement principle of OPCs is based on the dependence of light scattering on particle size. However, this dependence is not monotonic at all sizes as light scattering also depends on the particle composition (i.e., the complex refractive index, m ) and morphology. Therefore, the conversion of the measured scattered intensity to the particle size depends on the microphysical properties of the sampled aerosol population and might not be unique at all sizes. While these complexities have been considered before, corrections are typically applied ad hoc and are not standardized. This paper addresses this issue by providing a consistent and extended database of pre-computed correction factors for a wide range of complex refractive index values representing the composition variability in atmospheric aerosols. These correction factors are calculated for five different commercial OPCs by assuming Mie theory for homogeneous spherical particles and by varying the real part of the complex refractive index between 1.33 and 1.75 in steps of 0.01 and the imaginary part between 0.0 and 0.4 in steps of 0.001. The datasets are distributed for data users and geophysicists using number size distribution measurements from OPC for their research on atmospheric aerosols. Application and caveats of the correction factors are discussed, and key recommendations are provided to ensure the robustness and consistency of size distribution datasets.

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BibTeX
@article{formenti2026lookup,
  title = {{Look-up tables for complex refractive index correction of particle sizes measured by common research-grade optical particle counters}},
  author = {Paola Formenti and Claudia Di Biagio},
  journal = {Aerosol Research},
  year = {2026},
  month = sep,
  doi = {10.5194/ar-4-429-2026},
  url = {https://hal.science/hal-05756014v1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Look-up tables for complex refractive index correction of particle sizes measured by common research-grade optical particle counters
AU  - Paola Formenti
AU  - Claudia Di Biagio
PY  - 2026
DA  - 2026/09/16
JO  - Aerosol Research
DO  - 10.5194/ar-4-429-2026
UR  - https://hal.science/hal-05756014v1
AB  - Optical particle counters (OPCs) are widely used to measure the aerosol particle number size distribution over a large size range encompassing sub- and super-micron diameters. The measurement principle of OPCs is based on the dependence of light scattering on particle size. However, this dependence is not monotonic at all sizes as light scattering also depends on the particle composition (i.e., the complex refractive index, m ) and morphology. Therefore, the conversion of the measured scattered intensity to the particle size depends on the microphysical properties of the sampled aerosol population and might not be unique at all sizes. While these complexities have been considered before, corrections are typically applied ad hoc and are not standardized. This paper addresses this issue by providing a consistent and extended database of pre-computed correction factors for a wide range of complex refractive index values representing the composition variability in atmospheric aerosols. These correction factors are calculated for five different commercial OPCs by assuming Mie theory for homogeneous spherical particles and by varying the real part of the complex refractive index between 1.33 and 1.75 in steps of 0.01 and the imaginary part between 0.0 and 0.4 in steps of 0.001. The datasets are distributed for data users and geophysicists using number size distribution measurements from OPC for their research on atmospheric aerosols. Application and caveats of the correction factors are discussed, and key recommendations are provided to ensure the robustness and consistency of size distribution datasets.
SN  - 2940-3391
ER  -