Influence of Black Carbon on Measurement Errors in Scattering-Based Visibility Meters

Visibility is a fundamental meteorological parameter critical for surface transportation, aviation, maritime navigation, and weather process investigation. Scattering visibility meters are extensively utilised for their simple design and rapid response; however, their measurement principle is inherently limited, as they only quantify the scattering coefficient without assessing the absorption coefficient, potentially causing measurement errors. The World Meteorological Organisation (WMO) posits that the atmospheric absorption coefficient is usually relatively small and can be neglected, justifying the approximation of the extinction coefficient by the scattering coefficient. However, as black carbon is the predominant light-absorbing component in the atmosphere, an increase in its mass concentration markedly alters the atmospheric absorption coefficient, considerably impacting the accuracy of scattering-based visibility meters. Based on Mie scattering theory and incorporating both field observations and laboratory data, we systematically examined the effects of black carbon and its interactions with other aerosol components on the measurement errors of scattering visibility meters. Our findings revealed that the impact of black carbon on measurement errors is substantial, and under certain conditions, particularly pronounced. This influence is not only dependent on the mass concentration of black carbon but also closely associated with aerosol size distribution, mixing state, and the characteristics of other scattering aerosols. Due to the spatiotemporal variability of these factors, the impact of black carbon on visibility errors is uncertain. Therefore, during the calibration of scattering-based visibility meters, the effects of black carbon and its associated factors must be considered to enhance measurement accuracy. We propose calibration recommendations for scattering-based visibility meters aimed at reducing measurement errors and improving the accuracy of visibility assessments.