Brown carbon (BrC) has a substantial direct radiative effect, but current estimates of its impact on radiative balance are highly uncertain due to a lack of measurements of its light-absorbing properties, such as mass absorption efficiency (MAE). Here, we present a new analytical paradigm based on a Bayesian inference (BI) model that takes multiwavelength aethalometer measurements and total carbon data to resolve the concentrations of black carbon and BrC, and MAEs of BrC on a sample-by-sample basis. Hourly MAEs, unattainable in previous studies, can now be calculated, enabling the first-time observation of the darkening-bleaching dynamics of BrC in response to photochemical transformation.
View Article and Find Full Text PDFParticulate matter emitted from vehicles (PM) represents a major air pollution source in urban areas. Ambient measurements of hopanes and elemental carbon have traditionally been coupled with the Chemical Mass Balance (CMB) model to quantify the contributions to fine PM from diesel and gasoline vehicular emissions (VE). The organic carbon part of PM, however, undergoes gas-particle partitioning and oxidation degradation as VE move from exhaust pipe to receptor sites.
View Article and Find Full Text PDFVehicular emissions (VE) are among the major sources of airborne fine particulate matter (PM) in urban atmospheres, which adversely impact the environment and public health. Receptor models are widely used for estimating PM source contributions from VE (PM), but often give inconsistent results due to different modelling principles and assumptions. During December 2015-May 2017, we collected nine-months of hourly organic carbon (OC) and elemental carbon (EC) data, as well as 24-h PM speciation data including major species and organic tracers on select days from an ad hoc roadside site in Hong Kong.
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